A Proposed Heuristic for a Computer Chess Program · A proposed heuristic for a machine playing the...

A Proposed Heuristic for a Computer Chess Program

copyright (c) 2010John L. Jerz

[email protected], Virginia

Independent ScholarMS Systems Engineering, Virgina Tech, 2000

MS Electrical Engineering, Virgina Tech, 1995BS Electrical Engineering, Virginia Tech, 1988

August 24, 2010

Abstract

How might we manage the search efforts in a computer chess program in orderto play a stronger positional game of chess? A new heuristic for estimating thepositional pressure produced by chess pieces is proposed. We evaluate the ’health’of a game position from a Systems perspective, using a dynamic model of theinteraction of the pieces. The identification and management of stressors and theconstruction of resilient positions allow effective cut-offs for less-promising gamecontinuations due to the perceived presence of adaptive capacity and sustainabledevelopment. We calculate and maintain a database of potential mobility for eachchess piece 3 moves into the future, for each position we evaluate in our searchtree. We determine the likely restrictions placed on the future mobility of thepieces based on the attack paths of the lower-valued enemy pieces. Initial searchefforts are based on goals obtained from the lowest-scoring of the vital diagnosticindicators. Initial results are presented.

keywords: complexity, chess, game theory, constraints, heuristics, planning,measurement, diagnostic test, resilience, orientor

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A Proposed Heuristic - copyright (c) 2010 John L. Jerz

Contents

1 Overview 4

2 Introduction 4

3 Principles of Positional Chess 5

4 Systems Engineering 6

5 Systems Thinking 7

6 Goldratt’s Theory of Constraints and Thinking Process 8

7 Soft Systems Methodology 9

8 Measurement 10

9 Vulnerability 11

10 Resilience 11

11 Inventive Problem Solving 14

12 The Strategic Plan 15

13 From Orientors to Indicators to Goals 21

14 Shannon’s Evaluation Function 24

15 The Positional Evaluation Methodology 25

16 All Work and No Play... 30

17 John Boyd’s OODA Loop 32

18 Adaptive Campaigning model of Grisogono and Ryan 33

19 Results 33

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20 Conclusions 36

21 Appendix A: Related Quotations 40

References 42

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1 Overview

The complexity present in the game of chess of-ten hinders planning efforts and makes simplequestions like ”what’s going on?” and ”whichside has the better position?” difficult to answer.

Indeterminate and unexpected events in thenear future might make revisions necessary forthese plans, often after only a few moves havebeen played.

We theorize that dynamic planning modelsbased on perceptions of constraints, the man-agement of stress, the readiness of resourcesto support strategy, resiliency, sustainable de-velopment, and sensitivity to both incrementalprogress towards goals and the emergence of newopportunities can be used with greater success.We seek positions which can serve as a platformfor future success, in a future that is often un-certain.

A proposed heuristic for a machine playingthe game of chess, taking advantage of conceptsfrom multiple disciplines, can be used to bet-ter estimate the potential of resources to supportstrategy and to offer better insight for determin-ing whether progress is being made towards re-mote goals. In a future that is uncertain, thereis a benefit to develop a strategic position fullof resilience, flexibility, and structures with thepotential for seizing new opportunities as theyemerge.

As we evaluate each game position and ori-ent our search efforts, we now consider the poten-tial to exploit and respond to new opportunitiesas time passes and new situations emerge frombeyond our initial planning horizon. Our flexibil-ity ideally allows a smooth and resilient responseto concurrent events as they unfold. We theo-rize that our focus on the constraints, as well as

the development of a resilient position, is a moreuseful level of abstraction for our game-playingmachine.

We examine concepts and values useful forplaying a positional game of chess, we developa perception useful for measuring incrementalprogress towards goals, and then look at po-sitions in chess games where the heuristic of-fers insight not otherwise obtainable. We con-clude that our orientation/evaluation heuristicoffers promise for a machine playing a game ofchess, although our limited evidence (at present)consists of diagrams showing the strategic (dy-namic) potential of the game pieces and an ex-ample of how these ’building blocks’ can be com-bined into vital indicators.

We see the chess position as a complex adap-tive system, full of opportunities of emergencefrom interacting pieces. Our aim in this paperis to reengineer the work performed by our ma-chine, mindful of the values commonly adoptedby experts and the principles of Systems think-ing, so that it might be done in a far superiorway (Hammer and Stanton, 1995).

2 Introduction

This paper is concerned with heuristic algo-rithms. According to (Koen, 2003) a heuristicis anything that provides a plausible aid or di-rection in the solution of a problem but is inthe final analysis unjustified, incapable of justifi-cation, and potentially fallible. Heuristics helpsolve unsolvable problems or reduce the timeneeded to find a satisfactory solution.

A new heuristic is proposed which offersbetter insight on the positional placement ofthe pieces to a chess-playing computer program.

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The heuristic will have usefulness in the orienta-tion/evaluation methodology of a computer pro-gram, or as part of a teaching tool which explainsto a human user the reasons that one side or theother has an advantage in a chess game.

The heuristic involves constructing a tableof the future mobility for each piece, taking intoaccount the other pieces on the board, as wellas the likely constraints that these other piecesplace on this future movement. The heuristicconcept is described, and then examples are pre-sented from a software application constructedto demonstrate this concept.

Computer chess programs have historicallybeen weak in understanding concepts relating topositional issues. The proposed heuristic offers amethod to potentially play a stronger positionalgame of chess.

3 Principles of Positional Chess

Understanding the principles of positional chessis a necessary starting point before designingconcepts useful for a machine implementation.We select the relevant concepts of positionalchess which have been addressed by multiple au-thors.

(Stean, 2002) declares that the most impor-tant single feature of a chess position is the ac-tivity of the pieces and that the primary con-straint on a piece’s activity is the pawn struc-ture. (Znosko-Borovsky, 1980) generalizes thisprinciple by declaring that if two opposing piecesmutually attack each other, it is not the weakerbut the stronger one which has to give way. (Re-shevsky, 2002) notes that a good or bad bishopdepends on placement of the pawns. Piecesshould be ”working” and engaged, delivering the

full force of their potential and avoiding influ-ences which constrain. (Levy, 1976) discusses agame where a computer program accepts a posi-tion with an extra piece out of play, making a windifficult, if at all possible. Our evaluation shouldtherefore consider the degree to which a piece isin play or is capable of forcefully contributing tothe game.

Stean defines a weak pawn as one which can-not be protected by another pawn, therefore re-quiring support from its own pieces. This isthe ability to be protected by another pawn, notnecessarily the present existence of such protec-tion. Stean declares that the pawn structurehas a certain capacity for efficiently accommo-dating pieces and that exceeding that capacityhurts their ability to work together.

(Aagaard, 2003) declares that all positionalchess is related to the existence of weakness ineither player’s position. This weakness becomesreal when it is possible for the weakness to beattacked. The pieces on the board and theirconstraining interactions define how attackablethese weaknesses are.

(Emms, 2001) declares that is an advantageif a piece is performing several important func-tions at once, while a disadvantage if a piece isnot participating effectively in the game. Emmsteaches that doubled pawns can be weak if theyare attackable or if they otherwise reduce themobility of the pawns. Doubled pawns can con-trol vital squares, which might also mean deny-ing mobility to enemy pieces. Isolated pawnsrequire the presence of pieces to defend them ifattacked.

(Dvoretsky and Yusupov, 1996) argue thatcreating multiple threats is a good starting pointfor forming a plan. Improving the performanceof the weakest piece is proposed as a good way

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to improve your position as a whole.

(McDonald, 2006) gives an example of gooddoubled pawns which operate to restrict the mo-bility of the opponent’s pieces and are not easilyattackable. His view is that every position needsto be evaluated according to the unique featurespresent.

(Capablanca, 2002) and (Znosko-Borovsky,1980) speak of how the force of the chess piecesacts in space, over the chessboard, and throughtime, in sequential moves. Critical is the con-cept of position, which is valued by greater orlesser mobility plus the pressure exerted againstpoints on the board or against opponent’s pieces.Pre-eminence, according to Capablanca, shouldbe given to the element of position. We are alsoinstructed that the underlying principle of themiddle game is co-ordinating the action of ourpieces.

(Heisman, 1999) discusses the important el-ements of positional evaluation, including globalmobility of the pieces and flexibility.

(Albus and Meystel, 2001) have written thatthe key to building practical intelligent systemslies in our ability to focus attention on what isimportant and to ignore what is not. (Kaplan,1978) says that it is important to focus attentionon the few moves that are relevant and to spendlittle time on the rest.

The positional style is distinguished by po-sitional goals and an evaluation which rewardspieces for their future potential to accomplishobjectives. (Ulea, 2002) quotes Katsenelinboigenas saying that the goal of the positional style ofchess is the creation of a position which allowsfor development in the future. By selecting ap-propriate placement of pieces, combinations ide-ally will emerge. (Katsenelinboigen, 1992) fur-

ther describes the organizational strategy of cre-ating flexible structures and the need to createpotential in adaptive systems that face an un-predictable environment.

(Botvinnik, 1984) and (Botvinnik, 1970) at-tempt in general terms to describe a vision forimplementing long range planning, noting thatattacking the paths that pieces take towards ob-jectives is a viable positional strategy. Positionalplay aims at changing or constraining the attackpaths that pieces take when moving towards ob-jectives - in effect, creating or mitigating stressin the position.

(Hubbard, 2007) identifies procedures whichcan be helpful when attempting to measure in-tangible values, such as the positional pressureproduced by chess pieces. (Spitzer, 2007) de-clares that what gets measured get managed,that everything that should be measured, canbe measured, and that we should measure whatis most important.

4 Systems Engineering

A system (Kossiakoff and Sweet, 2003) is a setof interrelated components working together to-ward a common objective. A complex engineeredsystem is composed of a large number of intri-cately interrelated diverse elements. von Berta-lanffy is of the opinion (von Bertalanffy, 1968)that the concept of a system is not limited tomaterial entities but can be applied to any wholeconsisting of interacting components. This de-scription could also apply to the situation facedby an agent playing a game, where the piecesrepresent the interrelated diverse elements. vonBertalanffy further identifies dynamic interac-tion as the central problem in all fields of reality

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(which would include playing a game), identi-fying system elements in mutual interaction asthe very core issue. Additionally, we are told tosuspect systems or certain systems conditions atwork whenever we come across something thatappears vitalistic or human-like in attribution.We therefore see an opportunity to apply prin-ciples of System Theory, and in particular, Sys-tems Engineering, to game theory.

How would we begin? We now apply basicprinciples of Systems Engineering from (Kossi-akoff and Sweet, 2003):

A needs analysis phase defines the need fora new system. We ask ”Is there a valid needfor a new system?” and ”Is there a practical ap-proach to satisfying such a need?” Critically, canwe modify existing designs, and is available tech-nology mature enough to support the desiredcapability? The valid need would be to playa stronger positional game of chess, and exist-ing technology has struggled with the conceptof positional chess, as reflected in recent corre-spondence games which use Shannon-based pro-grams. It would seem that we need a differentapproach, which might be as simple as attempt-ing to emulate the style of play performed bystrong human players.

The concept exploration phase examines po-tential system concepts in answering the ques-tions: ”What performance is required of the newsystem to meet the perceived need?” and ”Isthere at least one feasible approach to achiev-ing such performance at an affordable cost?” Wewould answer the first question as simply thatour software function as an adequate analysistool, capable of selecting high-quality positionalmoves (with quality of move proportional to theanalysis time spent) when left ”on” for indefi-nite periods of time. As far as the second ques-

tion, we might speculate that a new approachis needed, which feasibly we could model afterhumans playing the game.

The concept definition phase selects the pre-ferred concept. It answers the question: ”Whatare the key characteristics of a system conceptthat would achieve the most beneficial balancebetween capability, operational life, and cost?”To answer this question a number of alterna-tive concepts might be considered and their rel-ative performance, operational utility, develop-ment risk, and cost might be compared. The firstconcept we might consider would be the Shannonapproach, which has been the backbone of mostsoftware computer chess programs. We presentin this paper, defined in another section, anotherapproach. We therefore decide to explore theconcept definition phase in more detail, as welook for key system characteristics which con-ceptually could serve as the base of such a newsystem.

5 Systems Thinking

The heart of Systemsthinking, which is differentfrom analytical thinking,is the attempt to simplifycomplexity.

Systems think-ing is a disciplinefor observing wholes(Senge, 2006). Itis a framework forobserving interrela-tionships rather thanthings, for observing the effects of change ratherthan static snapshots. The heart of Systemsthinking, which is different from analyticalthinking, is the attempt to simplify complex-ity (Gharajedaghi, 2006). We see an opportu-nity to apply principles of Systems thinking togame theory. (Gharajedaghi, 2006) discusses

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how independent variables are the essence ofanalytical thinking. We might find, on closerinspection, that our independent variables arenot truly independent - that the whole is morethan a simple sum of the parts. Emergent prop-erties of a system are a product of interactionsand cannot (Gharajedaghi, 2006) be analyzedor manipulated by analytical tools, and do nothave causal explanations. We must instead at-tempt to understand the processes that producethem by managing the critical interactions. Onemight think of emergent properties as being inthe process of unfolding. What makes it possi-ble to turn the systems approach into a scientificapproach is our belief that there is such a thingas approximate knowledge (Capra, 1988).

(Gharajedaghi, 2006) informs us that un-derstanding consequences of actions (both short-and long-term, in their entirety), requires build-ing a dynamic model to simulate the multiple-loop, nonlinear nature of the system. Our modelshould aim to capture the important delays andrelevant interactions among the major variables,but need not be complicated.

We therefore attempt to approach the ori-entation/evaluation methodolgy from a Systemsperspective. We will look at the interactions ofthe pieces and their ability to create and miti-gate stress. We adopt constraints, vulnerability,dynamic modeling, and resiliency as higher levelconcepts which will help cut through the com-plexity and steer search efforts along the linesof the most promising moves. The technique ofmodeling (Kossiakoff and Sweet, 2003) is one ofthe basic tools of systems engineering, especiallyin situations where complexity and emergenceobscure the basic facts in a situation.

From (Anderson and Johnson, 1997), we ap-ply Systems thinking to look at the web of in-

terconnected, circular relationships present in achess position, confident that this is the propertool for doing so. Our reason for believing thisis that everything in a chess position is (An-derson and Johnson, 1997) dynamic, complex,and interdependent. Things are changing all thetime, analysis is messy, and the interactions ofthe pieces are all interconnected.

we apply Systems think-ing to look at the webof interconnected, circularrelationships present in achess position, confidentthat this is the proper toolfor doing so.

As we attemptto construct resilientgame positions, wefollow (Tierney andBruneau, 2007) andidentify 4 systemlevel components ofresiliency: Robust-ness - the ability of our game-playing agent towithstand our opponent’s forces without degra-dation or loss of performance; Redundancy -the extent to which pieces, structures or movesare substitutable, that is, capable of sustainingoperations, if degradation or a surprise move oc-curs; Resourcefulness - the ability of our agent todiagnose and prioritize candidate moves and toinitiate solutions by identifying and mobilizingappropriate amounts of search time and gameresources; and Rapidity - the capacity to restoreor sustain functionality in a timely way, contain-ing losses by graceful failure and avoiding otherdisruptions.

6 Goldratt’s Theory of Con-straints and Thinking Pro-cess

Goldratt (Goldratt and Cox, 2004) has devel-oped a Theory of Constraints which postulates

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that organizations and complex systems are hin-dered from reaching their goals by the con-straints placed on that system. Identifyingthose constraints and removing them can speedprogress towards these goals. (Scheinkopf, 1999)describes how Golratt’s institute began to mod-ify the original concepts to serve the needs ofclients who wanted more generalized proceduresto solve a wider variety of problems outside of afactory production environment.

Goldratt’s ideas, while seemingly original,can be properly classified as a Systems thinkingmethodolgy which emphasizes raw human think-ing over the construction and implementationof computer models. Each approach is useful.Also emphasized is a vocabulary and terminol-ogy which allows groups to construct and discussanalytical diagrams of feedback loops and iden-tify root causes.

(Dettmer, 2007) explores Goldratt’s Think-ing Process and identifies procedures to logicallyidentify and eliminate undesirable effects fromsystems and organizations.

(Dechter, 2003) explains that a model of re-ality based on constraints helps us to achieve aneffective focus for search efforts, and is similar tothe heuristic process that humans use to searchfor effective solutions in complex situations. Re-moving the constraints partially solves the prob-lem, and measured progress towards removingthese constraints can steer and prune our searchefforts when identifying positions and lines ofanalysis which are promising.

(Hollnagel et al., 2006) speak of identifyingand monitoring the ”barriers” which keep thesystem response within safe margins. Also, theuse of ”audit tools” is envisioned as a method tomeasure the effectiveness of the containment.

7 Soft Systems Methodology

(Checkland and Poulter, 2006) present a modi-fied Systems methodology where complexity andconfusion are tackled through organized explo-ration and learning. We envision the continuouschange present in the game of chess as a com-plex state that needs to be (at least partially)understood in order to make exploration efforts(of an exponentially growing search tree) moreefficient.

We conceptualize a learning agent whichgathers relevant information as it seeks to de-termine the cumulative stress present in the po-sition, in order to determine the paths of ex-ploration - the ones of promise and the onesof risk mitigation. Our Systems model (makingup our orientation/evaluation methodology) willideally suggest to us what moves are promisingor worth our time exploring, as well as to rec-ommend which paths can, justifiably, wait untillater. The heuristics which make up this learn-ing and decision making process will be discussedin a later section. Critical to these heuristics isthe concept that all dynamic behavior emergesfrom a combination of reinforcing and balancingfeedback loops (Anderson and Johnson, 1997).

what we really need is aninsightful and informed di-rection for exploration anda notion for how press-ing this direction becomesstrategically.

Curiously, ourorientation/ evalua-tion ’function’ willbecome a method-olgy rather than aformula. We shareBotvinnik’s puzzle-ment with an evaluation ”number” (Botvinnik,1970) when what we really need is an insightfuland informed direction for exploration (orienta-tion) and a notion for how pressing this direction

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becomes strategically.

The insight we obtain by this method is usedas a spring for action (Checkland and Poulter,2006), as our software agent decides what to donext, after completing the current evaluation.Our ”evaluation” ideally produces candidate di-rections for exploration, as part of a carefullyconstructed strategic plan, and indicates whichpaths are critical and which can wait until later.For Checkland, our model is an intellectual de-vice we use to richly explore the future, usingstress transformation as our chosen strategy, orworldview. Simply put, our model tells us whichpaths to explore.

Our estimate of the winning chances of acandidate position critically depends on the iden-tification and exploration of the critical candi-date sequences of moves, and the correct classifi-cation of the worthiness (for timely exploration)of such candidate positions. A heuristic estimateof the cumulative stress present in the position,at the end of our principal variation, can be cor-related, if desired, with winning chances. How-ever, our operational use of this value is for (cy-bernetically) steering search efforts.

8 Measurement

What gets measured gets managed. (Spitzer,2007) speaks about the critical need to developmetrics which are predictive and which measurestrategic potential. We seek to measure how”ready” our pieces are (and the structures theyform) for supporting strategy (Kaplan and Nor-ton, 2004), especially when the future positionswe face are not entirely determinable. An as-set (such as a game piece) that cannot supportstrategy has limited value. Part of our orien-

tation/evaluation of the promise of a positionshould ideally include the readiness of the piecesand structures to support future developments.We embrace the principle that what you look foris what you find.

For (Zeller and Carmines, 1980), measure-ment clarifies our theoretical thinking and linksthe conceptual with the observable. For mea-surement to be effective, we must first constructa valid sensor. In our attempts at measurement,we seek empirical indicators which are valid, op-erational indicators of our theoretical concepts.We desire to construct a diagnostic indicatorwhich gives, as a result, a useful predictive mea-sure of future promise and a direction for futureexploration.

Although it would seem that a perceptionbased on simplicity would yield the best all-around results, (Blalock, 1982) points out thedifficulties trying to simultaneously achieve sim-plicity, generality, and precision in our measure-ment. If we have to give up one of these three, itis Blalock’s opinion that parsimony, or the scien-tific idea that the simplest explanation of a phe-nomenon is the best one, would have to be sac-rificed in order to achieve the other two. There-fore, our attempts to describe a complex orien-tation/evaluation methodology are grounded inthe two-fold goals of generality (it must be ap-plied to all positions we encounter) and preci-sion (otherwise, search efforts are wasted on lesspromising lines).

The alternative view is presented by (Gun-derson et al., 2010), who declare that experiencehas suggested to be as ruthlessly parsimoniousand economical as possible while still retainingresponsiveness to the management objectives andactions appropriate for the problem. Addition-ally, we are advised that the variables selected

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for system description must be the minimumthat will capture the system’s essential qualita-tive behavior in time and space. We are furthercautioned that the initial steps of bounding theproblem determine whether the abstract modelwill usefully represent that portion of reality rel-evant to policy design. We must therefore aim tosimplify, but not so much as to impact the use-fulness of the tool for predicting promising pathsof exploration.

9 Vulnerability

Critical to the success of a computer chess pro-gram that attempts to play in the positionalstyle is the concept of vulnerability. The piecesand structures that are or have the potential tobecome vulnerable will become a focus of oursearch and exploration efforts and will serve astargets for our long-range planning.

We follow (McCarthy et al., 2001) and con-ceptualize vulnerability as a function of expo-sure, sensitivity, and adaptive capacity. Conse-quently, the sensor we develop should attemptto measure exposure to threats, the sensitivityto the effects of stimuli, and the ability to adaptand cope with the consequences of change. Weenvision a sensor that produces a forecast of po-tential vulnerability as an output. This forecastcan guide exploration efforts by identifying tar-gets for the useful application of stress and serveas one indicator of a promising position.

Additionally, we predict that any machine-based attempt to zero-in on vulnerability thatdoes not address this conceptual base runs therisk of missing opportunities in exploring theexponentially growing tree of possibilities thatexist for each game position. A missed oppor-

tunity might equally prevent us from increas-ing positional pressure on our opponent, or in-stead, might dissipate the pressure that we havecarefully accumulated over time. Our orienta-tion/evaluation of the winning chances presentin the position might not be as accurate as itcould be unless we explore the promising posi-tions and consider the vulnerabilities that arepresent.

We conceptualize that the reduction of vul-nerability and the pursuit of sustainable develop-ment are interrelated aims (Smith et al., 2003).

10 Resilience

When something unexpected happens, it is re-silience we fall back on - resilience provides thecapacity to sustain strategy change (Valikangas,2010). Vulnerability is the condition that makesadaptation and resilience necessary as a mitiga-tion (Worldwatch, 2009). The scientific studyof resilience began in the 1970s when NormanGarmezy studied well-adapted children who hadovercome the stress of poverty (Lukey and Tepe,2008). Resilience is also an important researcharea in military science (Friedl, 2007) and in thestudy of ecosystems (Folke et al., 2002). We findthis concept useful in game theory.

We desire a generic, con-tinuous ability (both duringcrisis and non-crisis gamesituations) to cope with theuncertain positions that ar-rive from beyond our plan-ning horizon.

In our view,adapted from(Luthar, 2003), re-silience refers to anongoing, dynamic de-velopmental processof strategically po-sitioning resourcesthat enables the player in a game to negoti-ate current issues adaptively. It also provides

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a foundation for dealing with subsequent chal-lenges, as well as recovering from reversals offortune.

We desire a generic, continuous ability (bothduring crisis and non-crisis game situations) tocope with the uncertain positions that arrivefrom beyond our planning horizon. Ideally, weseek to create a useful positional pressure to forcethese arriving positions to be in our favor, orminimally, to put a ”cage” of constraints aroundthe enemy pieces. Flexibility, adaptive capacity,and effective engagement of available resourceswill be our weapons against the dynamic changeswhich will unfold in our game (Hollnagel et al.,2008).

Ideally, we will look for and manage theheuristic early warning signs of a position ap-proaching a ”tipping point”, where a distinct,clear advantage for one side emerges from an un-clear array of concurrent piece interactions. Weagree with (Walsh, 2006) that resilience cannotbe captured as a snapshot at a moment in time,but rather is the result of an interactive processthat unfolds over time.

The failure to include resilience measure-ments like this in planning efforts might causea house-of-cards effect, as the weakest link inour plan might collapse, due to effects we cannotinitially perceive. This might create a situationfrom which we cannot recover, or from which wecannot continue to mount increasing positionalpressure on our opponent.

A central concept is the construction of aresilient position, one that ideally 1. possesses acapacity to bounce back from disruption in theevent of an unforeseen move by our opponent, 2.produces advantageous moves in light of smallmistakes by our opponent, or 3. permits us topostpone our search efforts at early points for

less promising positions, with greater confidencethat we have sufficient resources to handle futureunforeseen developments if the actual game playproceeds down that route. In simplest form, wemight just measure the ability to self-organize.

When change occurs, the components thatmake up resilience provide the necessary capac-ity to (minimally) counter and (ideally) seize newopportunities that emerge (Folke et al., 2002).Resilience is (minimally) insurance against thecollapse of a position and (ideally) an investmentthat pays dividends in the form of better posi-tions in the future. With no pun intended, wesee the struggle to control the unknown, emerg-ing future positions as a ”Red Queen’s Race”,where in tough-fought games against a talentedopponent, it might take all the effort possible tomaintain equal chances. Extraordinary effortsinvolving hundreds of hours of analysis per move(such as in correspondence games) might be re-quired to maneuver to an advantage (Jerz, 2007).

For (Reivich and Shatte, 2002), resilienceis the basic strength. (Hollnagel et al., 2006)suggest that ”incidents”, which for us might bethe construction of short sequences of just thetop few promising moves (diagnostic probing),might reveal insight to boundary conditions inwhich resilience is either causing the system tostretch to adapt, or buckle and fail. Emergencyresponse teams use practice incidents to measureresilience as unforeseen events emerge during op-erations. Fire drills, random audits and securitysearches, even surprise tests are diagnostic toolsused to detect and correct situations lacking inresilient capabilities.

We acknowledge the reality that our abilityto handle an unexpected move or critical situa-tion in a game depends on the structures alreadyin place (Weick and Sutcliffe, 2007). We desire

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(Weick and Sutcliffe, 2007) to pay close attentionto weak signals of failure that are diagnostic in-dicators of potential problems in the system. Wealso perform diagnostic probing to uncover andsteer game play towards positions where thereare multiple good moves - an additional sign ofresilience.

We speculate that the abil-ity to construct a resilientposition and the ability toperceive stress in a posi-tion are two primary con-ceptual differences betweena game-playing man andmachine.

We speculatethat the ability toconstruct a resilientposition and the abil-ity to perceive stressin a position are twoprimary conceptualdifferences betweena game-playing manand machine. We believe that these abilities canbe emulated through the use of custom diagnos-tic tests.

Humans construct resilient positions (instrategic situations) almost by instinct and of-ten without conscious thought (Fritz, 2003), indiverse situations such as driving automobiles,playing sports games, conducting warfare, socialinteraction, and managing resources in businessor work situations. Humans have such refinedabilities (Laszlo, 1996) to make predictions, in-terpret clues and manipulate their environment,that using them is frequently effortless, espe-cially if performed daily or over extended periodsof time. (Aldwin, 2007) points out that humansappear to be hard-wired physiologically to re-spond to their perceptions of stress - so much sothat effective responses can be generated contin-uously with little conscious thought. We there-fore see the machine-based perception of stressas critical to successful performance in a game.

Additionally, much has been written (Fagre

and Charles, 2009) (Folke et al., 2002) concern-ing ecosystems, resilience, and adaptive manage-ment that has direct application to game theory.

Conceptually, we desire the equivalent of a”mindset” that can successfully cope with prob-lems as they arise, as we attempt to 1. exam-ine the promising positions, 2. evaluate the cor-responding winning potential and 3. steer oursearch efforts through an exponentially grow-ing ”tree” of strategically important move se-quences. This process is aided by the heuristicmeasurement of adaptive capacity, as the thou-sands of unexamined positions that lie just be-yond the point of our search cut-offs must beresilient enough to counter whatever unknownevents emerge. Before we cut-off our search ef-forts, we might choose to search the less resilientpositions a little deeper as a mitigation of un-foreseen events.

Rather than thinking about resilience as”bouncing back” from a shock or stress, it mightbe more useful to think about ”bouncing for-ward” to a position where shocks and stresseshave less effect on vulnerabilities (Walsh, 2006)(Worldwatch, 2009). Integral to the definitionof resilience are the interactions among risk andprotective factors (Verleye et al., prepub) at anagent and environmental level. Protective fac-tors operate to protect assets, such as pieces in agame, at risk from the effects of the risk factors.

We agree and conceptualize that, while riskfactors do not automatically lead to negativeoutcomes, their presence only exposes a game-playing agent to circumstances associated witha higher incidence of the outcome; protective ormitigating factors such as constraints can con-tribute to positive outcomes - perhaps regardlessof the risk status.

We accept as an operational concept of re-

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silience, the fourth proposal of Glantz and Slo-boda (Glantz and Johnson, 1999), which involvesthe adoption of a systems approach. We con-sider both positive and negative circumstancesand both influencing and protecting characteris-tics and the ways in which they interact in therelevant situations. Additionally, this conceptu-alization considers the cumulation of factors andthe influences of both nearby and distant forces.In addition, (Elias et al., 2006) discuss a model ofresilience in which specific protective influences(which we see as constraints) moderate the ef-fect of risk processes over time, in order to fosteradaptive outcomes.

We propose (Gunderson et al., 2010) an ap-proach based on resilience, which would empha-size the need to keep options open, the need toview events in a larger context, and the need toemphasize a capacity for having a large numberof structural variations. From this we recognizeour ignorance of, and the unexpectedness of fu-ture events. The resilience framework does notrequire a precise ability to predict the future, butonly a capacity to devise systems that can absorband accommodate future events in whatever un-expected form they may take. If we could cramMacGyver into our software, we would certainlydo so.

11 Inventive Problem Solving

Our chess program attempts to be, like Mac-Gyver, an inventive problem solver. We see ef-fective problem solving as an adaptive processthat unfolds based on the nature of the problem,rather than as a series of specific steps (Albrecht,2007). We agree with (Browne, 2002) that know-ing the difference between what’s important andwhat isn’t is a basic starting point.

We attempt to navigate an exponentiallygrowing search tree, selecting those paths for ex-ploration that are promising, interesting, risk-mitigating, and resilient in the face of an un-known future. We are concerned at all timeswith the potential of a position to serve as an ad-vancing platform for future incremental progresstowards positional goals (Fritz, 2007). We willaccomplish this by knowing the outcomes wewant and looking tirelessly for them. (Savran-sky, 2000) lists three major requirements for aproblem-solving methodology, which we modifyslightly for the purposes of a machine playing agame:

1. It should focus on the most appropriateand strongest solutions

2. It should produce, as an output, the mostpromising strategies

3. It should acquire and use important, well-organized, and necessary information at all stepsof the process

(Savransky, 2000) additionally suggests thatwe should focus on gathering the important in-formation, information which characterizes theproblem and makes it clear, including contra-dictions. Any simplifications we perform shouldaim at reducing the problem to its essence andbe directed towards our conceptual, strategic so-lution.

As an example, typical American news re-ports each day announce the results of the DowJones index of stocks. This index serves as agood indicator of overall market performanceand can help answer the question ”How did themarkets do today?”. We seek an equivalent sum-mary numerical representation of reality (March,1994) which can serve as a guiding light and ameasure of progress towards our distant posi-

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tional goals. We are not restricted to the useof a single scoring metric, and can combine mul-tiple, critical metrics in creative ways, includingthe selection of the lowest score from several indi-cators to provide a search focus. We should firstform a concept of what should be measured, thencreate a sensor array which allows us to measureand perform search efforts (in an exponentiallygrowing tree of possible continuations) with rea-sonable efficiency.

12 The Strategic Plan

We see a strategic plan (Bradford et al., 2000)as a simple statement of the few things we re-ally need to focus on to bring us success, as wedefine it. It will help us manage every detail ofthe game-playing process, but should not be ex-cessively detailed. It will encapsulate our visionand will help us make decisions as we criticallychoose, or choose not, to explore future positionsin our search tree. We see the formation and ex-ecution of the strategic plan as the most effectiveway to get nearer to the goal state, especially ina competitive environment where our opponentis also attempting to do the same. The simpleprinciples that govern strategy are not chains butflexible guides leaving free play, in situations thatare themselves enormously variable (Castex andKiesling, 1994).

We see the role of themachine as merely that ofan executor of a strategicplan... we simply ask themachine to do what it istold.

We see the roleof the machine (inplaying a game suchas chess) as merelythat of an executorof a strategic plan,where we have previ-ously defined (through programmed software in-

structions) the specific answers to the questions”Where do we want to be?” ”How will we knowwe have reached it?” ”What is changing in theenvironment that we need to consider?” ”Whereare we right now?” and ”How do we get from hereto our desired place?” (Haines, 1998). In our vi-sion, the intelligence is located in the strategicanswers to these questions and in the skill of theprogrammer in implementing them - we simplyask the machine to do what it is told.

computers... cannot un-derstand symbols (or in-deed anything else either),though they can manipu-late symbols according toformal rules with consum-mate speed and accuracy,far surpassing our own fum-bling efforts... they donot understand the ques-tions they are asked orthe answers they provide.- Richard Gregory, Minds,Machines and Meaning, In:Rose, Appignanesi, Scienceand Beyond, 1986.

If one game-playing computerprogram is betterthan another, asdemonstrated in atournament of manygames played, wespeculate that thereason is either a bet-ter strategic plan ora better software im-plementation of thatplan. Therefore, im-provements in com-puter chess programsideally should focuson these two areas, including answers to thequestions presented above. For Haines, all typesof problems and situations (which include se-lecting a move in a game) can benefit from astrategic approach.

Before we develop our strategic plan, weask ourselves and ponder three critical questions(Jorgensen and Fath, 2007): 1. what are theunderlying properties that can explain the re-sponses we see on the game board to perturba-tions and interventions, 2. are we able to for-mulate at least building blocks of a management

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theory in the form of useful propositions aboutprocesses and properties, and 3. can we form atheory to understand the playing of chess that issufficiently developed to be able to explain obser-vations in a practical way for choosing a move?We do not see the need to construct mathemat-ical proofs - the concept of a useful propositionallows us flexibility in choosing an approach andallows us to consider multiple options before set-tling on one with the most promise. We returnto these critical questions whenever we seek di-rection or clarification in an approach, or con-sider starting over. We look to other disciplines(as suggested by (Boyd, 1987)) and to other pro-fessionals who have sought answers to the samequestions, which must be asked in a general wayto any management problem.

Central to our strategic plan are the follow-ing concepts (Jorgensen and Fath, 2007): systembehavior frequently arises out of indirect inter-actions that are difficult to incorporate into con-nected models, that we may not know exactlywhat happens, but approximately what happens,and that we can use holistic metrics to mea-sure the growth and development of a positionin a game. We acknowledge that systems have acomplex response to disturbances, and that con-straints play a major role in interactions. As astrategy we seek a method to determine (and toresolve uncertainty concerning) 1. the promis-ing candidate moves in a given position, and 2.the chances of sustainable development in a po-sition, allowing us to postpone (if necessary) theexploration of future consequences.

In a building block for ourstrategic plan, we exam-ine the position under in-spection for the presence ofstressors

In a buildingblock for our strate-gic plan, we exam-ine the position un-

der inspection for thepresence of stressors(Glantz and Johnson, 1999) and determine theircontribution to the cumulative stress in the po-sition. A stressor is a real object on the gameboard, such as a piece, or an object or prop-erty that might become real in the future, suchas a Queen from a promoted pawn, a stonein the game of Go, or a King in the game ofdraughts/checkers. Using our stressors, we seekto establish a structural tension (Fritz, 1989)that, if resolved, leads to positions that favor us.

The stress we seek to place on our oppo-nent (Glantz and Johnson, 1999) is the kind thatinterferes with or diminishes the developmentof our opponent’s coping repertoire, search andplanning abilities, expectations and potential re-silience. This stress is ideally so effective that wecreate a platform from which we can apply evenmore stress. We force our opponent to divertadditional resources to containing our threats,making fewer resources available for threats ofhis own.

We attempt to cope withthe stressors of our oppo-nent by weakening them orreducing their influence toa manageable level

We attempt tocope with the stres-sors of our opponentby weakening themor reducing their in-fluence to a manage-able level (Snyder,2001) - there is no compelling need to make theireffects go away completely. For (von Bertalanffy,1968), stress is a danger to be controlled andneutralized by adaptive mechanisms. We gatherdiagnostic information that is used to determinethe readiness of the pieces to inflict stress on theopponent and lessen the stress imposed by theenemy pieces on our weak points. The creation

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of effective stress and the perceived mobilizationof forces to mitigate it will become a central con-cept in our orientation/evaluation. Our orienta-tion/evaluation looks not so much to goal seek-ing/optimizing a ”score” as to sustaining rela-tionships between/among the pieces and learn-ing what happens as stress is moved from onearea of the board to another.

Figure 1: Simplified model (dynamic hypothesis) of posi-tional pressure for each piece

Figure 1 shows a simplification of the pro-posed model of positional pressure for each piece,based on principles of system dynamics. Thefuture mobility of each piece targets opponentpieces, the trajectories taken by these pieces, andcertain other weaknesses such as weak pawns,the opponent’s king, or undefended pieces. Thisthreat is mitigated (but not reduced completely)by the protective factor of constraints imposedby the lower-valued enemy pieces. The resid-ual ”Stock” is the effective stress that can befelt by our opponent, and which we seek to in-crease. For (Warren, 2008), the management ofcritical resources is part of an emerging theory ofperformance: performance depends on resourcecontribution, resource contribution accumulatesand depletes, and this depends on existing re-source contribution levels.

Figure 2 shows the plan for managing theperceived stress by incentivizing a coping strat-

egy, such as the placement of constraints, in or-der to control the effects of the overall cumulativestress. We seek to maintain a resilient positionfull of adaptive capacity.

Figure 2: As perceived stress increases, we increase the in-centive to cope with the stress

Things start to get complicated when weremove stress (and the associated constraints)from one area of the board and apply it toother areas. The short- and long-term effectsof these stress-exchanging maneuvers are exam-ined through prioritized search efforts, and in ouropinion represent the essence of playing a gamesuch as chess. This conceptual model will formthe basis of the machine’s perception. We relyon the simplifying principles of system dynam-ics to predict and anticipate the effects of suchstress transformation.

From (Friedl, 2007) we define a stressor asany challenge to a player in a game that evokesa response. Coping is the set of responses thatsustain performance in the presence of stressors.Resilience is the relative assessment of copingability. We desire to create in our opponent’s

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position a condition similar to fatigue, definedby Friedl (and modified for game theory) as thestate of reduced performance capability due tothe inability to continue to cope with stressors.We follow (Fontana, 1989) and define stress asa demand made upon the adaptive capacity ofa player in a game by the other. We theorizea correlation between the state of stress-inducedreduced performance capability and an ”advan-tage”, or favorable chances for the more capableplayer winning the game.

we are dealing with a pro-cess whose effects taketime in revealing them-selves

Strategically, weseek to identify thestress present in theposition by 1. exam-ining the demands ofeach stressor, 2. the capacity of each player torespond to those demands, and 3. the conse-quences of not responding to the demands.

we will predict the win-ning chances at some fu-ture point in time, afterthe present circumstancesprogress and the structuresin place are allowed to un-fold

We carefully de-fine weakness so thatthe stress and tensionwe create is focusedand effective. The in-formation we gatherfrom the interactingpieces should be pre-cise enough to get results - it does not need to beperfectly accurate. Information is power (Brad-ford et al., 2000), especially in strategic planning.Along the way, we will need to make assumptionsabout whether or not the stress we are inflictingon our opponent is increasing or decreasing, andwhether it is effective or not effective. We mightexplore promising paths in detail to confirm ourassumptions, or we might just rely on our mea-surements of resilience.

Critical is our ability to focus our search ef-forts on lines that are promising, with regardto the oriented application of stress and thepredicted effects on future lines of play. Inour opinion (Schumpeter, 2008), we are deal-ing with a process whose effects take time inrevealing themselves - we will predict the win-ning chances at some future point in time, af-ter the present circumstances progress and thestructures in place are allowed to unfold, includ-ing the newly emergent features which we are notcurrently able to perceive. We establish a port-folio of promising lines, and see where they go.We invest our time and processor resources inthe most promising, but only after investigatingthe promising via a swarm of lower-risk exper-iments (Hamel and Valikangas, 2003). We de-fine a concept of stress which lets us focus oursearch efforts on anticipated promising lines. Werely on the promise of adaptive capacity presentin resilient positions to sustain our efforts inlines where the perception of weaker cumulativestress, time constraints, and our model of pur-poseful activity do not permit us to explore.

Figure 3: Conceptual Framework, from Chapin, 2009, p.21,(placeholder until new diagram is created)

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We theorize that the dynamic forces ofchange during the playing of a game have anadaptation cost associated with them (Kelly andHoopes, 2004) (Zeidner and Endler, 1996). Thismight come from a shift in expectations, or froma required recovery from disruptions. We make”payments” for these adaptation costs from our”bank” of resilience. If we lose our positional re-silience, we lose our flexible ability to adapt tothe unknown requirements of change. Likewise,we can make ”deposits” to our resilience accountduring quiet periods of maneuver, if we choose,and if we value resilience as an element of our ori-entation/evaluation methodology. (Friedl, 2007)refers to this concept as pre-habilitation. We seekto attack our opponent’s capacity to respondand to strengthen our own, so that the dynamicforces of change that drive the game continua-tion will cause the unknown positions arrivingfrom beyond our planning horizon to be in ourfavor.

We seek a resilient mindset. Specifically, wefollow (Coutu, 2003) and aim for three funda-mental characteristics: we identify and face thereality of the stresses and constraints present inthe positions we evaluate, we identify and rewardthe values of positional chess, and we develop anability to improvise solutions based on whateverresources are available to us. We seek to preparefor an unknown future that can be influencedby the strategic placement of resources in thepresent.

In the generalized exchange of pieces,squares, and opportunities encountered in gameplaying (Botvinnik, 1970), we seek to establisha pressure that has a realistic chance to resolvein our favor, as determined by heuristic probingand the examination of promising future gamesequences. We desire to create and sustain a

web of stress which threatens to become real andtherefore has the property that (von Neumannand Morgenstern, 1953) have called ”virtual” ex-istence. Our opponent must ”spend” or dedi-cate resources to contain or adapt to the threats.Even if a particular threat is contained, it never-theless has participated in the dynamic shapingand influencing of the events that emerge andunfold in the game.

We will succeed at forming an effectivestrategic plan when we have identified our values,determined the key drivers to performance, de-veloped a sensor which is effective at measuringthem, and have focused on the lines of play thatare promising. At all times we wish to maintaina resilient position, which increases our ability toeffectively handle the unknown positions whichlie beyond the horizon of our explorations.

We will use two key strategies (Maddi andKhoshaba, 2005) to become and remain resilient:we will develop the vision to perceive changes inthe promise of a position (as they emerge fromour heuristic explorations), and we seek flexi-bility to act quickly, while remaining focusedon our goals of establishing and maintaining auseful structural tension. We seek (Kelly andHoopes, 2004) a balanced portfolio of resilienceskills, where ideally we are focused, flexible, or-ganized, and proactive in any given situation.We believe that resilient responses (Kelly andHoopes, 2004) are the result of resilience char-acteristics operating as a system, as we evaluateand predict the emergent results of change.

Following (Jackson, 2003), we avoid plac-ing a complete reliance on specific predictionsof the future, concentrating on relationships, dy-namism and unpredictability as much as we doon determinism. In our plan, we will adapt asnecessary and seize new opportunities as they

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emerge from the ”mess”. We seek to focus onidentifying and managing the structures that willdrive the behavior of the game, and acknowledgethe reality that large portions of the future pos-sibilities will go unsearched and unexplored (un-til they emerge from beyond our planning hori-zon and into our perception). As we deepen ourexploration and learning, we see new opportu-nities emerging as much for us as for our op-ponent, and requiring us to re-direct our search(and planning) efforts. We see the widest pos-sible spectrum of adaptive responses competingfor the fittest solution (Bossel, 1998). Diversityis an important prerequisite for sustainability.

Where possible, we follow the advice ofFrench military strategist Pierre-Joseph Bourcet(Alexander, 2002) and spread out attackingforces over multiple objectives, forcing an ad-versary to divide his strength and prevent con-centration. Such divided forces - a ”plan withbranches”, can be concentrated at will, espe-cially if superior mobility is present, as recom-mended by French military strategist Guibert.As an end result of all this positional pressureand maneuver, we seek what Napoleon sought,that is (Alexander, 2002), the nature of strategyconsists of always having (even with a weakerarmy) more forces at the point of attack or atthe point where one is being attacked than theenemy. Such positions have the possibility of thewin of material, and are then approached froma more tactical perspective - one that currentheuristics handle well.

From a high level, we visualize the oppo-nent’s pieces in the game of chess as a network,and agree with (Wilson, 2006) that the best wayto confront a network is to create a counternet-work, a non-hierarchical organization capable ofresponding quickly to actionable intelligence ob-

tained from our search tree. Networks are anessential ingredient in any complex adaptive sys-tem. Without interactions between agents, therecan be no complexity (Beinhocker, 2007).

We aim for control (Wylie and Wylie, 1989)(Kelly and Brennan, 2010), defined by (Mc-Cormick, 2005) as (1) the ability to see every-thing in one’s area of operation that might pose athreat to security and (2) the ability to influencewhat is seen. Our main efforts must be to es-tablish control. Once control is established, theopponent becomes an ineffective fighting force.Direct action does not provide control; controlprovides the ability to conduct effective directaction (Canonico, 2004).

We see the indirect approach as the most di-rect path to victory (Wilson, 2006) (Hart, 1991).

We cannot improve on the centuries-old ob-servation that the secret of all victory lies in theorganization of the non-obvious (Marcus Aure-lius). To accomplish this, we follow (Maslow,1987) and critically focus our attention on theunusual, the unfamiliar, the dangerous and thethreatening, while seeking (from necessity, andfor exploration purposes) to separate the dan-gerous positions from the safe.

We desire to create, in the words of Vickers(Allison and Zelikow, 1999) (Vickers, 1995), anappreciative system, where our value judgmentsinfluence what aspects of reality we care to ob-serve, which in turn are influenced by instrumen-tal calculations, since what we want is affected bywhat we think we can get. We seek to establisha readiness to distinguish and respond to someaspects of a system rather than others, and tovalue certain conditions over others. If our chessplaying agent can successfully act as a rationalactor, it is through the mechanism of an appre-ciative system that this is accomplished.

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13 From Orientors to Indica-tors to Goals

We identify and adapt the approach of (Bossel,1976) (Bossel, 1977) (Bossel, 1994) (Bossel,1998) (Bossel, 1999) (Bossel, 2007) and (Mullerand Leupelt, 1998) to conceptualize the ’health’and evaluation of a chess position, which in ourvision shares much with that of an ecosystem.We seek indicators which realize Bossel’s six ba-sic high-level orienting properties of existenceand subsistence, effectiveness, freedom of action,security, adaptability, and coexistence. We the-orize with Bossel that these properties are eachvital diagnostic indicators of successful systemdevelopment, and we aim to steer our initialsearch efforts along paths which seek to improvethe weakest of these properties. These indicatorsmust give a fairly reliable and complete pictureof what really matters (Bossel, 1998).

We have found six basicsystem orientors (existenceand subsistence, effective-ness, freedom of action, se-curity, adaptability, coex-istence) that apply to allautonomous self-organizingsystems -Hartmut Bossel

If a system isto be viable in thelong run, a minimumsatisfaction of eachof these basic orien-tors must be assured(Bossel, 1994). Wetheorize with Bosselthat the behavioralresponse of the system is conditional on the cho-sen indicator set: problems not perceived cannotbe attacked and solved (Bossel, 1977). Mean-ingful non-routine behavior can only occur byreference to orientors, which are therefore keyelements of non-routine behavior (Bossel, 1977).The possible successes of unoriented non-routinebehavior can be only chance successes (Bossel,1977).

Needs provoke real im-pulses for action... whensufficiently gratified ceaseto exist as active determi-nants or organizers of be-havior

We directly fol-low (Lockie et al.,2005) in our concep-tual foundation of in-dicators, in which wedirectly quote due tothe importance of theconcept. Indicators are instruments to defineand monitor those aspects of a system that pro-vide the most reliable clues as to its overall well-being. They are used, in other words, to providecost and time-effective feedback on the healthof a system without necessarily examining allcomponents of that system. According to propo-nents, the validity of indicators is based on thedegree to which the wider network of componentsand relationships in which they are situated linktogether in a relatively stable and self-regulatingmanner, and the degree to which the indicatorsthemselves represent the most salient or criticalaspects of the system that can be monitored overtime.

Health and fitness of a sys-tem require adequate satis-faction of each of the sys-tem’s basic orientors. Plan-ning, decisions, and actionsin societal systems musttherefore always reflect atleast the handful of basicorientors (or derived crite-ria) simultaneously. Com-prehensive assessments ofsystem behavior and devel-opment must also be multi-criteria assessments...

(Maslow, 1987)notes that needs,along with their par-tial goals, when suffi-ciently gratified ceaseto exist as active de-terminants or orga-nizers of behavior.Bisogno (Bisogno,1981) notes that theterm need means astate of dissatisfac-tion provoked by thelack of something feltas being necessary. Needs provoke real impulsesfor action. Importantly for Bisogno, needs which

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would appear to be essential in a particular mo-ment, are no longer so when these circumstances- time, place, (or for Maslow a state of satisfac-tion), change. A need becomes a necessity whenits satisfaction is absolutely indispensable to agiven state of affairs (Bisogno, 1981).

We see a value in the two-phased approach of(Bossel, 1976) and (Bossel, 1994): first, a certainminimum qualification must be obtained sepa-rately for each of the basic orientors. A deficitin even one of the orientors potentially threatensour long-term survival from our current position.Our computer software will have to focus its at-tention on this deficit. Only if the required min-imum satisfaction of all basic orientors is guar-anteed is it permissible to try to raise systemsatisfaction by improving satisfaction of individ-ual orientors further - if conditions, in particularour opponent, will allow this.

We see goal functions as operating to trans-late the fundamental system needs expressed inthe basic orientors into specific objectives linkingsystem response to properties observed on thechess board. We conceptualize that goal func-tions emerge as general properties in the coevo-lution of the chess position and dynamic, futuredevelopment. They can be viewed as specific re-sponses to the need to satisfy the basic orientors.For example, mobility is related to adaptabil-ity, constraints relate to coexistence, king safetyis related to the orientors of security and exis-tence, virtual existence and stress are related toeffectiveness, material is related to existence, se-curity and adaptability, etc. We can creativelycome up with new indicators for our search ori-entation, but we see them fitting within the pro-posed framework and ’dimension of concern’ asoutlined previously.

...a system’s developmentwill be constrained by theorientor that is currently’in the minimum’. Par-ticular attention will there-fore have to focus on thoseorientors that are currentlydeficient. -Hartmut Bossel

We see the vi-tal orientors, whichexpress our values,as operating togetherto create a selectionmethod for our im-mediate goals. Thegoals we seek are notspecific objects, butrather changes in our relations or in our oppor-tunities for relating (Vickers, 1995).

We see an interesting similarity with the”ABC” (airway, breathing, circulation) prioritysystem used in emergency room and rescue op-erations when deciding what to do next withan accident victim. The rescue team performsthe set of vital diagnostic tests and then focusestheir immediate attention on the critical indica-tor that scores the lowest. The ”health” of thevictim (and in fact the direction to take next)would not be based on an average or summa-tion score of the vital indicators, but instead onthe vital indicator which scores the lowest. Thegoal, then would be to do something which im-proves the score returned by that indicator. Ifmore than one indicator is below a certain criti-cal threshold (such as, the patient is not breath-ing and there is no circulation), then Cardiopul-monary Resuscitation (CPR) would need to beperformed - improvement of the airway, breath-ing and circulation indicators are all simultane-ously attempted.

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Our experience in computerchess over the past fewyears seems to indicate thatfuture chess programs willprobably benefit from eval-uation functions that alteras the general chess envi-ronment changes. -PeterFrey, Chess Skill in Manand Machine, 1977

We also see asimilarity to the com-mon yearly perfor-mance evaluationwhich is traditionallyperformed by Amer-ican management oneach company em-ployee, or even a re-port card given to astudent. The ritualevaluation will list strengths, weaknesses, andexpectations, and it is also common to list im-provements necessary to reach the next perfor-mance level. The smart worker will examine hisvital, multi-criteria diagnostic assessment andorient his or her efforts (during the next year)towards improving the weakest scoring of theseindicators, while continuing to leverage strengthsand meet the listed expectations.

We see similarities to Festinger’s principleof cognitive dissonance (Festinger, 1957), wherea perception of dissonance between an observedindicator and a desired value leads to activityoriented towards reduction of the perceived dis-sonance. Festinger believes that reduction of dis-sonance is a basic process in humans, precededfirst by perception and identification.

We see the chess programs of the future asaddressing this conceptual foundation, in cre-ative ways and approaches that cannot yet beenvisioned by today’s developers. Our concep-tualization of stress management and the con-struction of resilient positions as indicators are,ideally, part of an operational realization of thesix orientors. If our concept fails as an orientor orfocus of search efforts, then it needs to be modi-fied or itself re-engineered. Perfectly usable indi-cators might overlap, or require too much proces-

sor time to implement. Perhaps what is requiredis the art of a talented programmer/chess playerto select a set of indicators which also orient witheffective insight.

What we are saying is simply that we mustpay attention to each of these orientating qual-ities separately - we should not just roll themup into a grand, universal ”number” and expectto effectively and efficiently drive our search ef-forts in that fashion. A weakness in one of thesix orientors critically impacts sustainable devel-opment in the uncertain future and cannot be”made up for” with a higher score from the oth-ers. A simple mechanism for scoring, such asaveraging the lower two indicators (of six total,one for each orientor), or using the lowest if it isfar beneath the others, will make sure that themachine pays attention to (and focuses attentionon) those orienting parameters that are in needof improvement.

Our immediate goals,therefore, emerge from theweakest indicators (results)of the vital diagnostictests, and operate tofocus the search effortsalong lines that allowsustainable development inthe uncertain future.

Our orienting in-dicators, which helpus to construct a pic-ture of the state ofour environment onwhich we can baseintelligent decisions(Bossel, 1998), canall be based on acommon foundation,such as cumulative stress, but with a weight-ing that aims to highlight the particular dimen-sion of concern. Our goal is simply to determine,through appreciative indicators, ”What mattersmost now?” (Vickers, 1995) and then to (ini-tially) focus attention on any move which we per-ceive to make progress in that area or dimensionof concern.

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Our present [evaluationfunction] is blind to thesimplest phenomena. Theevaluator gladly acceptsa position in which thecomputer is a knight aheadalthough its king is out inthe center of the boardsurrounded by hostileenemy queens and rooks.-David Slate and LawrenceAtkin, Chess Skill in Manand Machine, 1977

What good is be-ing a piece up if yourKing is in the cen-ter of the board, sur-rounded by hostileenemy pieces? Bet-ter to see if we canreturn the King toa safe place, even atthe price of material,so that we can con-tinue the sustainabledevelopment of ourposition in the fu-ture. We therefore orient our attention and fu-ture searching in ways to improve King safety.Our immediate goals, therefore, emerge from theweakest indicators (results) of the vital diagnos-tic tests, and operate to focus the search effortsalong lines that allow sustainable developmentin the uncertain future. The orientors representour wants or intentions - an intention doesn’t ex-actly require any deep calculation or plan. Wecan explore the moves that (partially) satisfy ourwants, and by simple focused learning, examinethe consequences of what emerges as we slide for-ward a few promising moves into the future.

14 Shannon’s Evaluation Func-tion

Shannon proposed (Shannon, 1950) a simpleevaluation to be performed in relatively quies-cent positions. While recent tournaments haveshown that such evaluations (combined withalpha-beta pruning and the null-move heuristic)can be used to produce world-class chess pro-grams, we seek an alternate approach with the

capability of even better performance. Programsthat use Shannon’s evaluation often have troublefiguring out what to do when there is no directsequence of moves leading to the placement ofpieces on better squares (such as the center), orthe acquisition of a ”material” gain.

Networks are comprised ofa set of objects with directtransaction (couplings)between these objects...these transactions viewedin total link direct andindirect parts together inan interconnected web,giving rise to the networkstructure...

We see a gen-eral correlation be-tween the placementof a piece on a”good square” andthe ability of thatpiece to inflict stresson the opponent, andto mitigate the ef-fects of stress causedby well-placed oppo-nent’s pieces. We even see that the concept ofmobility has value in a general sense. However,we see problems with this technique being usedto build positional pressure, such as the kindneeded to play an effective game of correspon-dence chess. The long and deep analysis pro-duced by the machine is often focused in thewrong areas, as determined by the actual courseof the game.

The stress produced by the Shannon methodis not of the type that reduces the coping ca-pacity of the opponent, or increases our own re-silience, in certain game situations where posi-tional play is required. For example, in positionsthat are empty of tactical opportunities, the ma-chine can be effectively challenged by opponentswho know how to play a good positional game ofchess (Nickel, 2005). The terms of the Shannonevaluation function do not seem suitable metricsfor guiding search and planning efforts, in thesecases.

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...The connectivity of na-ture has important impactson both the objects withinthe network and our at-tempts to understand it.If we ignore the web andlook at individual uncon-nected organisms... wemiss the system-level ef-fects. -Jorgensen, Fath, etal., A New Ecology, p.79

(Fontana, 1989)advises us to ask:what are the stres-sors, what needs tobe done about them,and what is stoppingus from doing it?There is little to begained from general-izing, if our goal is toidentify the stressors,accurately assess thelevels of stress present, and mobilize accordingto the results.

15 The Positional EvaluationMethodology

We propose that an approach which attempts toincrease the oriented positional pressure or cu-mulative stress on the opponent, even if unre-solved at the terminal positions in our searchtree, is a viable strategy and has the potentialto play a world-class game of chess. Our strate-gic intent is to form targeted positional pressure(aimed at weakpoints defined by chess theoryand at constraining the movement of the enemypieces) that will resolve at some future point intime into better positions, as events unfold andgameplay proceeds. At minimum, this pressurewill allow for sustainable development as onecomponent of a resilient position. We will notjudge pieces by the ”squares” they occupy, butinstead, by our heuristic estimate of the level offocused stress they can contribute (or mitigate)in the game.

We construct an orientation/evaluationmethodology with the goal of making our ma-

chine more knowledgeable with regard to thepositional concepts discussed earlier. In design-ing our orientation/evaluation methodology, weheed the advice of (Dombroski, 2000) that thismethodology is our test of effects and conse-quences and is our guiding light in our searchfor the consequences of our choices.

So we need something likea map of the future. A mapdoes not tell us where wewill be going, or where weshould be going - it merelyinforms us about the pos-sibilities we have... Wetherefore need a descriptionof the possibilities ahead ofus...

Our orienta-tion/evaluation cen-ters on a heuristic ap-praisal of the stresswe inflict on the op-ponent’s position,and our mitigationof the stress createdby the opponent. Weaim to reduce our op-ponent’s coping abil-ity through careful targeting of stress. The dy-namic forces of change, acting over time and ina future we often cannot initially see, transformthe reduced coping ability of our opponent, ourcarefully targeted stress, and our resilient posi-tion full of adaptive capacity, to future positionsof advantage for us.

Perhaps this concept is what inspired BobbyAllison to race most of the 1982 Daytona 500without a back bumper - it fell off after contact-ing another car early in the event (NASCAR,2009). Some drivers accused Allison’s crew chiefof rigging the bumper to intentionally fall offon impact. Allison’s car without the bumperhad improved aerodynamics, and the forces ofdynamic change operating over the 500 milerace supplied the driver with an advantage heused to win. Other examples (the winged keelof the Australia II yacht and the new loop-keel design, hinged ice skates and performance

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enhancing swimsuits come to mind) show howsmall changes, combined with other critical abil-ities and interacting with a dynamic environmentover time, can create a performance advantage.

...Such a map would nothave to give us very de-tailed information... But itshould give us a useful im-age of what may be ahead,and allow us to comparethe relative merits of dif-ferent routes... before weembark on our journey. -Hartmut Bossel

We seek, in sim-ilar fashion, to fa-vor certain inter-acting arrangementsof pieces, such thatthe dynamic forcesof change (operat-ing during the play-ing of a game) causefavorable positionsto emerge over time,from beyond our initial planning horizon. Weseek to re-conceptualize the ”horizon effect” toour advantage. We cannot arrange for a bumperto fall off during a chess game, but we can dothe equivalent - we can actively manage the dy-namics of change to improve the chances for per-sistence or transformation (Chapin et al., 2009).This would include the general approaches of re-ducing vulnerability, enhancing adaptive capac-ity, increasing resilience, and enhancing trans-formability (Chapin et al., 2009). We managethe exposure to stress, in addition to the sensi-tivity to stress (Chapin et al., 2009).

We adopt the vision of (Katsenelinboigen,1992), that we define a ”potential” which mea-sures a structure aimed at forcing events in ourfavor. Ideally, one which also absorbs or reducesthe effects of unexpected events.

We follow the suggestion in (Pearl, 1984)to use as a strategy an orientation/evaluationbased on a relaxed constraint model, one thatideally provides (like human intuition), a streamof tentative, informative advice for managing the

steps that make up a problem-solving process,and use the insight from (Fritz, 1989) and (Ster-man, 2000) that structure influences behavior.

Sustainability... means, assaid before, that only theriverbed, not the exact lo-cation of the river in it, canand should be specified -Hartmut Bossel

In order to moreaccurately estimatethe distant positionalpressure produced bythe chess pieces, aswell as to predict thefuture capability ofthe pieces in a basic form of planning (Lakein,1974) (Shoemaker, 2007) we create the softwareequivalent of a diagnostic probe which performsa heuristic estimate of the ability of each pieceto cause and mitigate stress. The objectiveswe select for this stress will be attacking enemypieces, constraining enemy pieces, and support-ing friendly pieces (especially those pieces thatare weak). To support this strategy, we calculateand maintain this database of potential mobilityfor each chess piece 3 moves into the future, foreach position we evaluate.

We update this piece mobility database dy-namically as we evaluate each new leaf positionin our search tree. This database helps us de-termine the pieces that can be attacked or sup-ported in the future (such as 2 moves away fromdefending a piece or 3 moves away from attack-ing a square next to the enemy king), as well asconstrained from accomplishing this same activ-ity. Note that the piece mobility we calculate isthe means through which we determine the pres-sure the piece can exert on a distant objective.We can therefore see how mobility (as a generalconcept) can become a vital holistic indicator ofsystem health and one predictor of sustainabledevelopment.

We reduce our bonus for each move that it

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takes the piece to accomplish the desired objec-tive. We then consider restrictions which arelikely to constrain the piece as it attempts tomake moves on the board.

For example, let’s consider the pieces in thestarting position (Figure 4).

Figure 4: White and Black constraint map, pieces at thestarting position Legend: Red: pawn constraints, Yellow: Mi-nor piece constraints, Green: rook constraints, Blue-green:Queen constraints, Blue: King constraints

What squares can our knight on g1 influencein 3 moves, and which squares from this set arelikely off-limits due to potential constraints fromthe enemy pieces?

Figure 5: Influence Diagram and Simulation Diagram, Ng1at starting position Legend: Red - 1 move influence, Yellow- 2 move influence, Green - 3 move influence, Dark Red - 1move influence possibly constrained by opponent piece, DarkYellow - 2 move influence possibly constrained by opponentpiece, Dark Green - 3 move influence possibly constrained by

opponent piece, Blue - no influence possible within 3 moves, X- presence of potential constraint ”it is what it does... how cansomething exercise any efficacy by means of its mere existencein space, without any motion?” - Johann Gottlieb Fichte

We now construct the influence diagram(Shoemaker, 2007) and the simulation diagram(Bossel, 1994) (Figure 5), which are interpretedin the following way. If a piece is on our influ-ence diagram for the knight, then it is possibleto attack it or defend it in 3 moves (this includeswaiting moves or moves which move a piece outof the way). We label this kind of map an influ-ence diagram because it shows the squares thatthe piece can influence in 3 moves, provided thatit is unconstrained in movement by the enemy.

Keep in mind that we need to take into ac-count the location of the other pieces on thechessboard when we generate these diagramsfor each piece. If we trace mobility through afriendly piece, we must consider whether or notwe can move this piece out of the way before wecan continue to trace mobility in that particu-lar direction. If we trace mobility through anenemy piece, we must first be able to spend 1move capturing that piece.

Comparing this 3-move map with a diagramof the starting position, we can determine thatthe white knight on g1 can potentially attack 3enemy pieces in 3 moves (black pawns on d7, f7and h7). We can defend 8 of our own pieces in 3moves (the knight cannot defend itself).

We decide to reward pieces for their poten-tial ability to accomplish certain types of worth-while positional objectives: attacking or con-straining enemy pieces, defending friendly pieces,attacking squares near our opponents king (es-pecially involving collaboration), minimizing ouropponent’s ability to attack squares near our ownking, attacking pieces that are not defended orpawns that cannot be defended by neighboring

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pawns, restricting the mobility of enemy pieces(specifically, their ability to accomplish objec-tives), etc. In this way, we are getting real aboutwhat the piece can do. The bonus we give thepiece is 1. a more precise estimate of the piece’sability to become strategically engaged with re-spect to causing or mitigating stress and 2. op-erationally based on real things present on thechessboard. In this way, our positional orienta-tion/evaluation methodology will obtain insightnot usually obtained by a computer chess pro-gram, and allow our machine to take positive,constructive action (Browne, 2002). It is still anestimate, but the goal here is to focus our searchefforts on likely moves in a positional style ofplay, and to evaluate positions from a more po-sitional point of view.

What does the orientation/evaluationmethodology look like for the proposed heuris-tic? We model (and therefore estimate) thepositional pressure of our pieces, by following atwo-step process:

1. We determine the unrestricted future mo-bility of each chess piece 3 moves into the future,then

2. We estimate the operating range or levelof engagement of the pieces by determining thelimiting factors or constraints that bound the un-restricted mobility.

The concept of using limiting factors isbriefly mentioned (Blanchard and Fabrycky,2006) in the context of Systems Engineering.(Lukey and Tepe, 2008) argue that an impor-tant aspect of cognitive appraisal is the extent towhich stress-causing agents are perceived as con-trolled. Balancing processes such as constraints(Anderson and Johnson, 1997) seek to counterthe reinforcing loops created by a piece creatingstress, which, if unconstrained, can potentially

create even more stress (perhaps in combina-tion with other pieces). Once we have identifiedthe limiting factors, we can more easily exam-ine them to discover which ones can be alteredto make progress possible - these then becomestrategic factors.

The consideration of constraints is a partof the decision protocol of Orasanu and Con-nolly (Orasanu and Connolly, 1993) and (Pless-ner et al., 2008) which also includes the iden-tification of resources and goals facing the de-cision maker. We therefore reduce the bonusfor accomplishing objectives (such as, attackingan enemy piece or defending a friendly piece) ifthe required moves can only be traced throughsquares that are likely to result in the piece be-ing captured before it can accomplish its objec-tive. We also reduce the engagement bonus formobility traced through squares where the pieceis attacked but not defended. We may use an-other scheme (such as probability) for determin-ing stress-application reduction for piece move-ment through squares attacked both by friendlyand enemy pieces where we cannot easily re-solve whether or not a piece can trace mobilitythrough the square in question (and thereforecreate stress). We think in terms of rewarding aself-organizing capacity to create stress out of thevaried locations of the pieces and the constraintsthey face (Costanza and Jorgensen, 2002).

We reward each piece for its predicted abil-ity to accomplish strategic objectives, exert po-sitional pressure, and restrict the mobility of en-emy pieces, based on the current set of pieces onthe chess board at the time we are calling ourorientation/evaluation methodology. Using an-ticipation as a strategy (van Wezel et al., 2006)can be costly and is limited by time constraints.It can hurt our performance if it is not done with

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competence. An efficient compromise betweenanticipative and reactive strategies would seemto maximize performance.

We give a piece an offensive score based onthe number and type of enemy pieces we can at-tack in 3 moves - more so if unconstrained. Wegive a piece a defensive score based on (1) howmany of our own pieces it can move to defend in 3moves and (2) the ability to mitigate or constrainthe attacking potential of enemy pieces. Again,this bonus is reduced for each move it takes toaccomplish the objective. This information is de-rived from the influence diagram and simulationdiagram we just calculated. Extra points can begiven for weak or undefended pieces that we canthreaten.

The proposed heuristic also determines kingsafety from these future mobility move maps.We penalize our king if our opponent can movepieces into the 9-square template around ourking within a 3 move window. The penalty islarger if the piece can make it there in 1 or 2moves, or if the piece is a queen or rook. We pe-nalize our king if multiple enemy pieces can at-tack the same square near our king. Our king isfree to move to the center of the board - as long asthe enemy cannot mount an attack. The incen-tive to castle our king will not be a fixed value,such as a quarter pawn for castling, but ratherthe reduction obtained in the enemy’s ability tomove pieces near our king (the rook involved inthe castling maneuver will likely see increasedmobility after castling is performed).

The king will come out of hiding naturallywhen the number of pieces on the board is re-duced and the enemy does not have the poten-tial to move these reduced number of pieces nearour king. We are likewise free to advance thepawns protecting our king, again as long as the

enemy cannot mount an attack on the monarch.The potential ability of our opponent to mountan attack on our king is the heuristic we use asthe basis for king safety. Optionally, we will con-sider realistic restrictions that our own pieces canmake to our opponent’s ability to move piecesnear our king.

Pawns are rewarded based on their chance toreach the last rank, and what they can do (piecesattacked and defended in 3 moves, whether ornot they are blocked or movable). The piecemobility tables we generate should help us iden-tify pawns that cannot be defended by otherpawns, or other pieces - it is this weakness thatwe should penalize. Doubled or isolated pawnsthat cannot be potentially attacked blockaded orconstrained by our opponent should not be pe-nalized. Pawns can be awarded a bonus basedon the future mobility and offensive/ defensivepotential of a queen that would result if it madeit to the back rank, and of course this bonus isreduced by each move it would take the pawn toget there.

The information present in the future mo-bility maps (and the constraints that exist onthe board for the movement of these pieces) al-low us to better estimate the positional pres-sure produced by the chess pieces. From thesecalculations we can make a reasonably accu-rate estimate of the winning potential of a posi-tion, or estimate the presence of positional com-pensation from a piece sacrifice. This orienta-tion/evaluation score also helps steer the searchprocess, as the positional score is also a measureof how interesting the position is and helps usdetermine the positions we would like to searchfirst.

In summary, we have created a model of po-sitional pressure which can be used in the ori-

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entation/evaluation methodology of a computerchess program. (Michalewicz and Fogel, 2004)remind us that models leave something out, oth-erwise they would be as complicated as the realworld. Our models ideally provide insight andidentify promising paths through existing com-plexity.

(Starfield et al., 1994) emphasize that prob-lem solving and thinking revolve around themodel we have created of the process understudy. We can use the proposed model of po-sitional pressure to direct the machine to focusthe search efforts on moves which create the moststress in the position as a whole. For our searchefforts, we desire a proper balance between ananticipatory and a reactive planning strategy.We desire our forecast of each piece’s abilities tohelp us anticipate its effectiveness in the game(van Wezel et al., 2006), instead of just reactingto the consequences of the moves.

By identifying the elements and processes inour system (Voinov, 2008), identifying the limit-ing factors from the interactions of the elements,and by answering basic questions about space,time and structure, we describe and define theconceptual model of our system.

16 All Work and No Play...

...makes ”Jack” a dull boy. We follow (Brown,2009) and (Sutton-Smith, 2001) in a conceptual-ization of play that will form one foundation ofour automated search/exploration efforts.

Humans adopt play as a foundational be-havior that guides exploratory activity and insome cases becomes a basis for acquiring knowl-edge. Play is the basis of all art, games, books,sports, movies, fashion, fun, and wonder (Brown,

2009). Play is the vital essence of life - it is whatmakes life lively (Brown, 2009). However, a ma-chine does not know how to play. It simply doeswhat we tell it to do, so we must tell it how toplay with the pieces on the board and the re-lationships among these pieces. Why must ourmachine play? because, Play is the answer tothe question, How does anything new ever comeabout? (Jean Piaget).

We further conceptualize play (Sutton-Smith, 2001) as the extrusion of internal men-tal fantasy into the web of external constraints.Additionally, we adopt the practical aspect thatplay seems to be driven by the novelties, excite-ments, or anxieties that are most urgent to theperceptions of the players (Sutton-Smith, 2001).Finally, we note that the imagination makesunique models of the world, some of which leadus to anticipate useful changes - the strategicflexibility of the imagination, of play, and of theplayful is the ultimate guarantor of our game-based survival (Sutton-Smith, 2001). Play liesat the core of creativity and innovation (Brown,2009).

We desire our machine to always be busymaking up its own work assignments (Paley,1991). Specifically, the ”work assignments” in-volve choosing our courses of action and adjust-ing those courses based on the internal satisfac-tions we receive (Henricks, 2006). We desire fromour machine a behavior similar to ”playfulness”,and a set of creative, inquisitive, exploratory ori-entations centered on an object-based model ofthe game-world (Henricks, 2006). We desire anactivity of constant exploration, manipulationand appraisal. We seek to manage the explo-ration of the new. This conceptually involvesthe creation of small-scale experiments that canbe run outside the mainstream management sys-

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tems and learned from (Valikangas, 2010).

We agree with (Brown, 2009) that movementis primal and accompanies all the elements ofplay we are examining. Through movement play,we think in motion - movement structures ourknowledge of the world, space, time, and our re-lationship to others (Brown, 2009).

Our exploration of future game positionstherefore must take into account piece movementthat is likely, critical, interesting, stress induc-ing/relieving, or otherwise ”lively”. Children atplay engage other children or contemplate waysto engage their playmates. We therefore desireto create a heuristic which playfully examinesthe future consequences of the transformation ofstress on the board, as the pieces move about orare constrained by objects on the board, such asblocked pawns or lower-valued enemy pieces.

We begin our efforts by conceptualizing thebuilding of a principal variation a few moves intothe future, by first examining the single movethat creates the most perceived stress for our op-ponent (or mitigates the perceived stress causedby his pieces). We then look at the most likelyresponse.

After ”sliding forward” a few moves in thisfashion, we then work backwards in our princi-pal variation, examining the consequences of thenext most likely move, and so on. We establisha few simple rules: we orient our search efforts(initially) along the lines of improving the scoreof the weakest, vital diagnostic test. We performa search ”cut-off” only after we confirm that theposition in question is resilient and the movesleft unexamined are not the most promising (andremain so), after performing a shallower search.We seek to focus our efforts on uncovering thelikely future consequences of the most promisingextensions of managing the stress in the position.

Unexpected discoveries in the principal variationwill cause the machine to re-focus its efforts onthe next most promising lines. We then begin todeepen our search efforts and spend more timeexploring alternate moves in our principal varia-tion.

We additionally note positions where imbal-ances are created (using our vital diagnostic in-dicators) and investigate the consequences, espe-cially when efforts to return to a resilient positionrequire extra efforts.

For software testing and configuration pur-poses, we envision the use of automated tourna-ments of hundreds of games, each lasting perhapsa minute long, to assess and fix the parametersof these orientation/evaluation/search efforts sothat we might succeed in the widest number ofsituations. We envision a tool which identifiesand stores positions where faulty analysis wasgenerated. We see the programmer/developerexamining these saved positions and identifyingthe reason for the failure to orient/evaluate theindicated position.

We recognize certain positions as ”tacti-cal” in nature when responses become forced orwhen imbalances in vital indicators create fewbranches in our principal variation. We defer inthese cases to a search and evaluation method-ology designed for a more tactical situation.

We critically examine the trade-offs betweenexamining principal variations that are manymoves long, versus the exploration of the sec-ondary and tertiary lines that do not go as deep.We conceptualize our machine behaving like achild at play, creating novel combinations, andfinding or discovering what works and does notwork in an evolutionary fashion.

We can base our efforts on the ob-

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served behavior of large groups of Internet-connected humans examining a common chessposition, such as the daily chess puzzle featuredat http://www.chessgames.com/index.html (wehave no connection to the owners of this site -one of us (JLJ) pays a yearly fee to access cer-tain advanced site features).

17 John Boyd’s OODA Loop

The OODA Loop (Observe, Orient, Decide,Act) is a strategic methodology which wasoriginally applied by USAF Colonel JohnRichard Boyd to the combat operation pro-cess http://en.wikipedia.org/wiki/OODA loop.Boyd was of the opinion that without OODAloops, we will find it impossible to comprehend,shape, adapt to, and in turn be shaped by an un-folding, evolving reality that is uncertain, ever-changing, and unpredictable (Boyd, 1996). Boydadvocates an approach of pulling things apartand putting them back together until somethingnew and different is created (Boyd, 1987). Fur-ther, Boyd suggests we present our opponentswith ambiguous or novel situations in which theyare not capable of orienting their behavior orcoping with what’s going on (Boyd, 1987), whilewe maintain our fingerspitzengefuhl. For Boyd,orientation shapes the way we interact with theenvironment, and therefore the way we observe,decide, and act (Boyd, 2005). Boyd suggeststhat effective orientation demands that we createmental images, views, or impressions, hence pat-terns that match with the activity of our world(Boyd, 2005).

For Boyd, in a competitive encounter againsta talented opponent, our limited perceptionscause novelty to be produced continuously, andin an unpredictable manner. In order to main-

tain a competitive position we must match ourthinking and doing, hence our orientation, withthat emerging novelty. Yet, any orientation weassume prior to this emerging novelty is perhapsmismatched after the fact, possibly causing con-fusion and disorientation. However, Boyd pointsout, the analytical/synthetic process permits usto address these mismatches so that we can com-petitively rematch ourselves and thereby reorientour thinking and action with that novelty (Boyd,1992).

We therefore envision our search and explo-ration process as an operational realization ofthis concept. We observe information, unfoldingcircumstances and interactions, orient our be-havior according to Bossel’s concepts discussedearlier, decide which path to explore, and thenact by ”sliding forward” one move. We then re-peat the process, periodically ”backtracking” toexamine moves which were initially determinedto be the next best.

(Boyd, 1976) attempts to philosophically ar-rive at a theory useful for conducting warfareor other forms of competition, such as playinga game. Boyd concluded that to maintain acompetitively effective grasp of reality one mustoperationally follow a continuous cycle of inter-action with the environment oriented to assess-ing its constant changes. Boyd states that theOODA decision cycle is the central mechanism ofsuch adaptation, and that increasing one’s ownrate and accuracy of assessment (compared tothat of one’s opponent) provides a strategic foun-dation for acquiring an operational advantage ina dynamically changing environment.

Conceptually, we are exploring the presentand future consequences of the transformationof positional stress, with an emphasis on thesustainability of the intermediate positions, the

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http://en.wikipedia.org/wiki/OODA_loop


satisfaction of our operational needs, and (ul-timately) the perceived winnability of the finalposition. We are faced with a dynamic, novel,unstable world that we must constantly adaptto even as we try to shape it for our own ends(Hammond, 2001).

18 Adaptive Campaigningmodel of Grisogono andRyan

Grisogono and Ryan (Grisogono and Ryan,2007) propose the model of Adaptive Campaign-ing as a modified form of Boyd’s OODA loopthat presents a more relevant form for the chal-lenges of operating in an environment with highoperational uncertainty. Here we ’adapt’ theirapproach for game theory.

Adaptive Campaigning proposes a repeat-ing cycle of Act Sense Decide Adapt (ASDA).By placing ’Act’ first this model stresses theneed to act (make exploratory trial moves) withwhatever information is present, and by imme-diately following that with a ’Sense’ of what haschanged in our environment. The ’Decide’ func-tion follows to determine what is learned fromthe sensed feedback that results from the ac-tion, and what to do next - including possiblere-orientation based on results from the vital di-agnostic tests.

These first three elements of Adaptive Cam-paigning correspond closely to the four elementsof Boyd’s OODA loop, but with a different em-phasis on where the cycle starts, and with the’Orient’ function of OODA incorporated into the’Decide’ functions of ASDA. The object of the’decision’ is to choose the next trial move in ourforward exploration, or to begin backtracking by

exploring alternative moves in our principal vari-ation. So ’Adapt’, the fourth element of ASDA,explicitly adds the need to invoke adaptation andconsider what, if anything, should be changed onevery cycle, before continuing to the next cyclewith another external ’Act’.

Ideally, successful application of the ’Adapt’element results in the machine improving its abil-ity to focus/orient its efforts on the right objec-tives at the right time and in the right place.Modern combat, including game playing, cantherefore be characterized as competitive learn-ing in which all sides are constantly in a pro-cess of creating, testing and refining hypothesesabout the nature of the reality of which they area part (Kelly and Brennan, 2009).

Recent criticism of Adapting Campaigning(Thomas, 2010) claims that Boyd’s work ad-dresses the issues in question, and should be re-visited. Time will tell whether OODA or ASDAloops will prevail.

19 Results

We have created software to demonstrate cer-tain features of the proposed heuristic and nowexamine four positions to see if we can obtaina better positional understanding of how wellthe pieces are performing. John Emms (Emms,2001), reached Figure 6 as white (black to move)with the idea of restricting the mobility of black’sknight on b7.

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Figure 6: Emms-Miralles (Andorra, 1998) Constraint mapsLegend: The left diagram identifies the possible constraintsimposed by the white pieces, with red representing pawn con-straints, yellow minor piece constraints, green rook constraints,blue-green queen constraints, and blue king constraints. Theright diagram identifies possible constraints imposed by theblack pieces. The white and grey squares represent the stan-dard chessboard squares without constraints.

How fully engaged is this piece in the game?Let’s see what the influence diagram and simu-lation diagram from the proposed heuristic showus:

Figure 7: Emms-Miralles Tracing knight mobility from b7-a5-c4-b2 and b7-d8-e6-g5

Figure 8: Emms-Miralles Influence Diagram and SimulationDiagram for Nb7

We generate the constraint maps as in Fig-ure 6 in order to estimate the squares that theknight on b7 is likely to be denied access. Wethen apply the constraint maps to the individ-ual vectors which make up the influence diagramas in Figure 7 to create the simulation diagram.When a movement vector hits a constraint, fu-ture mobility through that square is constrained,and we use an ”X” to indicate constrained mo-bility. We can see from the X’s (denied potentialmobility) of Figure 8 that the movement of thepiece on b7 has been constrained. It is Emms’view that positional details like this one can bevitally important when assessing positions.

Figure 9: Constraint maps, white (left), black (right),Estrin-Berliner variation analysis (1965-68 corr.) after 12.Qe2Be6 13.Qf2, Black to move

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Figure 10: Influence Diagram and Simulation Diagram forBc1

Figure 11: King safety heuristic maps: left - black kingsafety, right - white king safety. In the left diagram, darkersquares are safer squares for the black king, while lighter col-ored squares are more dangerous.

Figure 9 examines a sideline from Estrin-Berliner (1965-68 corr.) after the proposed im-provement 12.Qe2 Be6 13.Qf2. How fully en-gaged is the white Bishop on c1? We generatethe constraint maps and influence diagram asbefore in order to construct the simulation di-agram. We see that the bishop on c1 can enterthe game after moving a pawn out of the way,and become useful for creating and mitigatingstress in future positions. Figure 11 displays anexperimental king safety heuristic which is gen-erated from all the piece influence diagrams anda rule which awards points based on number ofpieces which can attack a square and the dis-tance/constrained effort required to do so.

Figure 12: Constraint maps, white (left), black (right),Umansky-World correspondence game (2009)

Figure 13: Influence Diagram and Simulation Diagram forQe8

Figures 12 and 13 examine a positionfrom the recent Umansky-World correspondencegame. The constraint map gives insight to thecontrolling influences present on the squares, andthe influence diagram/ simulation diagram forthe Queen on e8 gives insight to what this piececan threaten in 3 moves. Note that this piececan influence square c1 via the difficult to findmove sequence e8 to e6-h6-c1.

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Figure 14: Influence Diagram and Simulation Diagramfor Rb8, Levy-Chess 4.4, simultaneous exhibition, 1975, after27.axb5

Figure 15: Influence Diagram and Simulation Diagramfor Rb8, Levy-Chess 4.4, simultaneous exhibition, 1975, after31.Bc8

Figures 14 and 15 show how a machine canpotentially recognize a trapped piece, with anexample first identified and discussed by (Levy,1976).

The elements of a systemand their interactions de-fine the system structure...

The computercan use the heuristicknowledge presentin the influence dia-gram and simulationdiagram to estimate the strategic potential orhow fully engaged each piece is in the game. Themaps are a useful holistic measurement of a ca-pacity to produce stress in a position, and can

be used as part of an oriented, vital system-levelindicator to predict and manage the sustainabledevelopment of a position in a chess game.

20 Conclusions

...By answering the basicquestions about space,time and structure, wedescribe the conceptualmodel of the system...Creating a conceptualmodel... very much re-sembles that of perception-Alexey Voinov

Alternative con-ceptual frameworksare important notonly for furtherinsights into ne-glected dimensionsof the underlyingphenomenon. Theyare essential as a re-minder of the distor-tions and limitations of whatever conceptualframework one employs (Allison and Zelikow,1999). Only by analyzing a phenomenon froman alternative perspective (preferably multiplealternative perspectives) can all the intricacies ofa situation be understood (Canonico, 2004). Ouralternative conceptual framework for machine-based chess can, at minimum, allow us deeper in-sight and better understanding of current meth-ods. Particularly in explaining and predictingactions, when one family of simplifications be-comes convenient and compelling, it is even moreessential to have at hand one or more simplebut competitive conceptual frameworks to helpremind us of what was omitted (Allison and Ze-likow, 1999). Allison and Zelikow believe this isa general methodological truth applicable in allareas of life, including, in our opinion, a strategyfor playing a game.

We agree with strategist Bernard Brodiethat strategy is a field where truth is soughtin the pursuit of viable solutions, not at all like

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pure science, where the function of theory is todescribe, organize, and explain and not to pre-scribe. The question that matters in strategyis: Will the idea work? (Steiner, 1991). Brodiebelieved that strategy was associated with prob-lems involving economy of means, i.e., the mostefficient utilization of potential and available re-sources (Steiner, 1991).

Ecosystems are working models of sustain-able complex systems, and it is reasonable tostudy them for clues to the sustainable manage-ment of the human enterprise (Jorgensen andMuller, 2000), including ’conflict ecosystems’mentioned by Kilcullen (Kilcullen, 2006). Weidentify systems thinking and the systems ap-proach as the theoretical basis for an orienta-tion/evaluation methodology, shifting our focusfrom the parts to the whole. The use of ap-proximate knowledge and the conceptualizationof a network of interacting components is real-ized through a system dynamics model of stress,or positional pressure.

The reality of the position on the chessboardis seen as an interconnected, dynamic web of re-lationships, with oriented, cumulative stress onedriving force of change. We seek resilient posi-tions and flexible, adaptive capacity (with thepromise of sustainable development) to counterthe effects of unknown positions that lurk justbeyond our planning horizon. The concepts oforientors and indicators, cumulative stress, con-straints and virtual existence allow us to effec-tively simplify the dynamic reality of each gamepiece interacting with every other game pieceon the board - to the point where we can pre-dict promising directions of exploration (via themechanism of stress transformation) and iden-tify the accessibility space (Bossel, 1998) of fu-ture sustainable development.

The properties of the partscan be understood onlyfrom the dynamics of thewhole. In fact, ultimatelythere are no parts at all.What we call a part ismerely a pattern in an in-separable web of relation-ships. -Fritjof Capra, TheRole of Physics in the Cur-rent Change in Paradigms

A model can beconsidered as a syn-thesis of elements ofknowledge about asystem (Jorgensenand Muller, 2000).Our model of dy-namic interactionpresented in this pa-per ideally capturesthe dominant vari-ables that control thetransformation of stress (Kossiakoff and Sweet,2003), omitting the higher order effects thathave a cost/benefit deemed to be overall noteffective. No models are valid or verifiable inthe sense of establishing their correctness (Ster-man, 2000) (Voinov, 2008). The question facingclients, academics, and modelers is not whethera model is true but whether it is useful as abasis for some action, which in our case, is steer-ing search efforts (through the critical lines) inan exponentially growing tree of possibilities,in a way that obtains actionable intelligenceand therefore allows a strong positional gameof chess to be played. (Miller and Page, 2007)advise, with regard to computational modeling,that we judge the quality and simplicity of themodel, the cleverness of the experimental de-sign, and examine any new insights gained bythe effort. We should also ask ourselves if ourmodel has just enough of the right elements, andno more. To be a good model, Miller is of theopinion that we have stripped phenomena downto their essentials, yet have retained enough ofthe details to produce the insights we require.

Ideally, our responsibility would be to usethe best model available for the purpose at hand(Sterman, 2000) despite its limitations. We

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view modeling (Sterman, 2000) as a process ofcommunication and persuasion among modelers,clients, and other stakeholders. Each party willjudge the quality and appropriateness of anymodel using criteria which reflect on their roleand perceived future benefits. This includes thetime and effort involved in the unending strug-gle to improve the model to the point where itsperformance reflects what theory would expectof the particular approach. Modeling team Amight not want to use a particular model due tosignificant time, money, belief, performance, andfamiliarity with their current approach. Team Amight not even be interested in discussing newapproaches. However, modeling team B mightbe looking for a new challenge, perhaps due todissatisfaction with the current model, a belief inpredicted performance, or perhaps due to a will-ingness to spend long hours and to engage withthe types of problems suggested by the new ap-proach. Team A might now become interested,seeing the preliminary success of team B.

Our attempts to reengineer the way ma-chines play chess are, in the true spirit of reengi-neering (Hammer and Stanton, 1995), throwingaway current methods and starting over, butplacing at the forefront of our design efforts thevalues and concepts of positional chess and Sys-tems thinking. We acknowledge the dynamicand static elements of a chess position, and con-struct a sensor array which responds to a per-ception of stress in the position in order to orientour efforts to effectively navigate and explore anexponentially growing search tree. We adopt aSoft Systems Methodology - that is, we see thegame position as complex and confusing, and weseek to organize the exploration of future conse-quences through the means of a learning system(Checkland and Poulter, 2006).

In the final analysis, per-ception seems to be the keyto skill in chess... The dif-ference between two play-ers [when one defeats theother in a game] is usu-ally that one looks at thepromising moves, and theother spends his time go-ing down blind alleys. -Neil Charness, Chess Skillin Man and Machine, 1977

The proposedheuristic offers in-sight on the abilityof the chess piecesto create and miti-gate stress and aimsfor a rich awarenessof discriminatory de-tail (Weick and Sut-cliffe, 2007) betweenpromising and lesspromising positions.We agree with Dono-hew, et al., (Donohew et al., 1978), that infor-mation seeking must be a primary method forcoping with our environment. Key componentsinclude the monitoring of structural tension cre-ated by the pieces as they mutually constraineach other and seek to satisfy vital system-levelneeds, and the attempt to create positions whichserve as a platform for future success, in a fu-ture that is uncertain. All sustainable activitieshave to accept the natural system of constraintsin which the investigated entity operates (Jor-gensen and Muller, 2000).

Our orientation/evaluation centers on an ar-ray of vital diagnostic appraisals of the cumula-tive stress each side inflicts on the opponent’sposition, and the perceived mitigation of suchstress. (Selye, 1978) considers stress to be an es-sential element of all our actions, and the com-mon denominator of all adaptive reactions. Weaim to reduce our opponent’s coping ability andadaptive capacity through oriented targeting ofstress. The dynamic forces of change, acting overtime and in a future we often cannot initially see,ideally transform the reduced coping ability ofour opponent, our carefully targeted stress, andour resilient position full of adaptive capacity, to

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future positions of advantage for us. The entirepurpose of modeling stress is to aid search focus- that is, we orient or focus our search effortsin priorities based on the changing amounts ofstress in the position (and the results of vitaldiagnostic tests). We additionally monitor thestress that threatens to become real, having theproperty that (von Neumann and Morgenstern,1953) have called ”virtual” existence. Even if thethreat does not materialize, it nevertheless hasthe capability to shape and influence the eventsthat do become real.

(Jorgensen, 2009) and (Bossel, 2007) discussthe application of Bossel’s orientor ideas to sim-ulated animals (animats) roaming in simulatedenvironments, where orientation rules are devel-oped over time to direct and control the behaviorof the simulated animal and optimize the acqui-sition of food and energy resources. These simu-lations involve the ’perception’ by the simulatedanimal of clues in the environment to the pres-ence of food as well as danger. We ask ourselveswhat orientation rules would develop if the sim-ulated environment were instead the board gameof interest. Might we then develop optimal rules(or minimally, a good set of rules) for orientingour search behavior for playing a board game?

We acknowledge that resilience is a dis-tinguishing characteristic of any successful sys-tem (Sanderson, 2009) (Gunderson et al., 2010).The creation of resilient positions full of adap-tive capacity allows us to sharply and effec-tively postpone search efforts in less-promisinglines with the low-risk promise of sufficient re-sources to ’MacGyver’ the unknown future thatlies beyond. We determine the level of resiliencepresent in a position using a set of (heuristic) vi-tal diagnostic tests, such as the ones proposed byBossel. We desire a methodology which emulates

a productive thinking process, such as one envi-sioned by (Hurson, 2008), but where we playfullyconsider responses that reflect the changing, ur-gent stress in the position, and the resilience ofthe less urgent positions and analysis lines leftunexamined.

Learning to handle a com-plex system means learningto recognize a specific setof indicators, and to assesswhat their current statemeans for the ’health’, orviability, of the system. Of-ten this learning of indica-tors is intuitive, informal,subconscious... - HartmutBossel

We configure oursearch/explorationactivity using the re-sults from automatedtournaments of 1-minute games.

From the high-est level, we desireto model the cumu-lative dynamic stresspresent in the posi-tion so that we can effectively explore the pos-sible directions of promising development. Ourestimate of winning chances critically dependsupon 1. exploring the promising and risk-mitigating paths and 2. correctly identifyingthose paths whose exploration of future conse-quences can justifiably wait until later. Inaccura-cies in these two areas of classification will createa limit to overall performance, as we strategicallyattempt to compete against other agents withdifferent and refined approaches to this sameproblem. We seek, as a strategy, to gain a sus-tainable edge over our opponent, and see thecareful formation and execution of the strategicplan as the best and most productive way to ac-complish this.

The proposed heuristic offers promise as acomponent of an orientation/evaluation method-ology for a computer chess program, and shouldbe used to steer a search process (such as for-ward and backward chaining) to effectively re-

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duce search depth for lines deemed less promis-ing.

The concepts of play, Boyd’s OODA Loop,and Grisogono and Ryan’s Adaptive Campaign-ing Model critically complete the conceptual-ization. We seek to ”play” the game of chessthrough a strategic orientation and explorationthat is guided by a playful examination of thefuture consequences of stress transformation, thetentative separation of positions into categoriesof safe (allowing a halt to further explorations)and dangerous (requiring additional searching),and vital diagnostic tests which summarize andsimplify the complexity present on the gameboard.

The presented results demonstrate the pos-sibilities of the proposed building blocks for fourtest positions. Perhaps chess is more than justcalculation (Aagaard, 2004), but the day maycome sooner than we think when computers useheuristics to play a positional game of chess atskill levels equal to their current strong tacticalplay. Correspondence chess would provide theideal testing ground for a positional heuristic.

We might borrow the words of economistJoseph Schumpeter (1883-1950) and theorizethat chess is a game of Creative Destruction.

Future work will investigate positions wherethe proposed heuristic does not work in provid-ing insight and direction in search efforts.

Note: colored diagrams were produced by a com-puter program in HTML format and rendered in a Fire-fox web browser in a method similar to that used by thesoftware program ChessDiagrams by Ambar Chatterjee.

Special thanks to all my friends at chessgames.com,

through whom I continue to learn about chess.

21 Appendix A: Related Quo-tations

The analysis of general system principles shows thatmany concepts which have often been considered asanthropomorphic, metaphysical, or vitalistic are ac-cessible to exact formulation. They are consequencesof the definition of systems or of certain system con-ditions. - Ludwig von Bertalanffy, General SystemsTheory, p.86.

a good model enables prediction of the futurecourse of a dynamic system. - Bruce Hannon andMatthias Ruth

Perception, motivation, and values combine tocreate choice. - Joe Vitale

It’s your decisions about what to focus on, whatthings mean to you, and what you’re going to do aboutthem that will determine your ultimate destiny. - An-thony Robbins

We are successful because we use the right levelof abstraction. - Avi Wigderson

We can influence the future but not see it. -Stewart Brand

The mind will not focus until it has clear objec-tives. But the purpose of goals is to focus your at-tention and give you direction, not to identify a finaldestination. - John C. Maxwell

Of all the factors that contribute to adapting tochange, the single most important factor is the degreeto which individuals demonstrate resilience - the ca-pacity to absorb high levels of change and maintaintheir levels of performance. - Mark Kelly and LindaHoopes

Every piece of business strategy acquires its truesignificance only against the background of that pro-cess and within the situation created by it. It mustbe seen in its role in the perennial gale of creativedestruction; it cannot be understood irrespective of itor, in fact, on the hypothesis that there is a perenniallull. - Joseph Schumpeter

It is not the strongest of the species that survive,not the most intelligent, but the one most responsive

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to change. - Charles Darwin

Resilience or some variation of this idea is aconcept that is explicitly if not tacitly implicit in al-most all explanatory models of behavior ranging fromthe biological to the social. It may be an inextricablepart of the ways in which we define and explain notonly human behavior but virtually all phenomena withvariable outcomes. - Meyer Glantz and Zili Sloboda

any approach able to deal with the changing com-plexity of real life will have to be flexible... It needsto be flexible enough to cope with the fact that ev-ery situation involving human beings is unique. Thehuman world is one in which nothing ever happenstwice, not in exactly the same way. This means thatan approach to problematical human situations hasto be a methodology rather than a method, or tech-nique... [Soft Systems Methodology] provides a set ofprinciples which can be both adopted and adapted foruse in any real situation in which people are intenton taking action to improve it. - Peter Checklandand John Poulter

I think that resilience is manifest competence de-spite exposure to significant stressors. It seems to methat you can’t talk about resilience in the absence ofstress. The point I would make about stress is the crit-ical significance of cumulative stressors. I think thisis the most important element. - Norman Garmezy

No plan survives contact with the enemy. - FieldMarshal Helmuth von Moltke

In many ways, coping is like breathing, an auto-matic process requiring no apparent effort... Is cop-ing always a conscious process? ...we so often mayrepeatedly respond to a recurring stressor that we loseour awareness of doing so. - Charles Richard Snyder

What business strategy is all about; what dis-tinguishes it from all other kinds of business plan-ning - is, in a word, competitive advantage. Withoutcompetitors there would be no need for strategy, forthe sole purpose of strategic planning is to enable thecompany to gain, as effectively as possible, a sustain-able edge over its competitors - Keniche Ohnae

Rykiel (1996) defines model credibility as ”a suf-ficient degree of belief in the validity of a model to

justify its use for research and decision-making.”...there is no use talking about some overall universalmodel validity; the model is valid only with respect tothe goals that it is pursuing - Alexey Voinov

A principal deficiency in our mental models isour tendency to think of cause and effect as localand immediate. But in dynamically complex systems,cause and effect are distant in time and space. Mostof the unintended effects of decisions leading to pol-icy resistance involve feedbacks with long delays, farremoved from the point of decision or the problemsymptom. - John Sterman

everything in nature, everything in the universe,is composed of networks of two elements, or two partsin functional relationship to each other... The mostfundamental phenomenon in the universe is relation-ship. - Jonas Salk, Anatomy of Reality

What is the core of the matter? Why should amachine not be an excellent chess player? Is the taskinsoluble in principle? ... No. The problem seems tobe soluble... The machine may play chess badly, likea beginning amateur, but the machine is not guilty.Man is guilty. He has not yet succeeded in teach-ing the machine, in transferring his experience to it.What is involved in teaching a machine to play chess?- Mikhail Botvinnik

once you become aware of what means the mostto you, you’re less likely to put off something that’sreally valuable for something that matters much less...it’s knowing the difference between what’s importantand what isn’t that allows us to solve problems effec-tively. - Joy Browne

I understand very well that a weakness is onlya weakness if it can be attacked, but you cannot putthis into the evaluation function. It is a matter ofsearch... To exploit them the program has to search.-Mathias Feist, Fritz programmer

Intelligence is the ability to acquire knowledge,and not the knowledge itself. - George F. Luger

Where sustainability is not even a goal, it is un-likely that sustainability will be achieved by accident.And even if it is a declared goal, sustainability can-

41


not be achieved where money, time, resources, and thecreative energies of individuals are wasted. -HartmutBossel

Additional quotes:

http://mysite.verizon.net/vzesz4a6/current/id201.html

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