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Hierarchical Temporal Memory Algorithms for Understanding Asymmetric Warfare

Jason Sherwin, Dimitri Mavris

Georgia Institute of Technology 270 Ferst Drive

Atlanta, GA 30332-0150 404-385-7485

jason.sherwin@gatech.edu Abstract—There is a problem in combat modeling when it comes to accurately representing asymmetric warfare. The roots of this problem stem from the ways in which warfare has evolved over the last century from force-on-force combat to the network-centric warfare (NCW) that has emerged today in its most exemplary fashion in the Middle East, especially in the context of Joint Urban Operations (JUO). Therefore, this paper focuses on some of the previous attempts as a means to motivate the need for a new technique that understands the interactions on which an accurate representation of NCW rests. Given the difficulties of previous modeling attempts, this work then focuses on the profound impact that human behavior has on the modeling of NCW. Furthermore, with such attention to human behavior, the discussion of modeling NCW shifts to the human brain and its impact on behavior in such an environment. Thus the topic of this paper turns towards what results from neuroscience are useful for understanding NCW and how those facts can be translated into use on modern computers, via the use of Hierarchical Temporal Memory (HTM) algorithms; finally, the inherent issues with such a task shall then be discussed. 1 2

TABLE OF CONTENTS

1. INTRODUCTION................................................................ 1 2. DEFINING NETWORK-CENTRIC WARFARE .................... 2 3. MODELING NETWORK-CENTRIC WARFARE.................. 2 4. EXAMPLE OF NETWORK-CENTRIC WARFARE............... 4 5. HIERARCHICAL TEMPORAL MEMORY ........................... 6 6. APPLYING HTMS TO NCW ............................................ 7 7. CONCLUSIONS ................................................................. 8 REFERENCES ....................................................................... 9 BIOGRAPHY ......................................................................... 9

1. INTRODUCTION As the United States has entered the 21st century, the rules of warfare have changed dramatically. The days of one nation declaring war on another nation have for the present time left us, and in their place there has grown a type of guerilla warfare that pits both dispersed assets and personnel against world superpowers. Furthermore, these confrontations occur in environments that are ever-1 1978-1-4244-2622-5/09/$25.00 ©2009 IEEE. 2 IEEEAC paper #1173, Version 2, Updated October 26, 2008

changing, yet highly influential on the outcome of the conflict. When there is such an imbalance between conflicting forces’ size in both personnel and assets, asymmetric warfare is the resulting phenomenon and it remains a challenge today as to how to deal with it from both a modeling and, consequentially, a strategic perspective.

An emerging distinction between how warfare is predominantly done today and how it was done in the past has been to notice the networked behavior of present day warfare versus its centralized modus operandi in the past. In particular, the distinction between warfighting today and in the past has emerged from the distinction between warfare in the Information Age and in the Industrial Age. The days of one force lining up on one side of the battlefield and another force lining up on the other side battlefield are no more; this was generally how warfare was done in the Industrial Age. However, today there is a decentralized command structure in place for forces that are just as distributed as their assets are in key areas of operation; this is how warfare is done in the Information Age and to highlight its distinction from the Industrial Age’s style of combat it has been termed network-centric warfare (NCW).

NCW is a general term that is used to distinguish warfighting in the past from its current form today. However, there is a particular example of NCW that is most pressing today in the current-day Middle East: Joint Urban Operations (JUO). In short, the aim of JUO is to defeat a network of adversaries in an urban environment without causing dilapidating damage to the society that already lives there. Quite obviously, such a task has its challenges, due in large part to the difference between the style of guerilla warfare encountered in JUO and that style of warfare in which one nation’s military is put against that of another’s. In particular, the distinguishing feature is that networked human behavior plays a dominant role in JUO – and by extension in NCW – so that if one does not understand the human element to warfare contributed by behavior then one is ill-equipped to deal with the clues that emerge before disastrous consequences are realized.

Having recognized a primary distinction between warfighting both today and in the past, it is surprising to realize that attempts to understand such warfare via

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modeling have not evolved suitably to the phenomena at hand. In this paper, an overview of the current state of combat modeling shall be given, with a particular emphasis on how the status quo of modeling is at odds with what is needed for the accurate modeling of NCW, and consequentially of JUO.

Building on the importance of human behavior in understanding NCW and previous modeling attempts’ lack of an ability to handle this seemingly unpredictable phenomenon, the focus of this paper shall then shift to what lessons from neuroscience can be used to add some understanding to the apparently intractable issue of human behavior in a networked environment. Consequently, an overview will be done of how the human brain as an information-processing device extracts meaning from time-evolving and disparate data, specifically focusing on how this ability is crucial to the development of Battlespace Awareness (BA) in JUO.

Such an overview though will be done in tandem with a discussion on how these results from neuroscience can be used in a modern computing context so that an understanding of NCW can emerge from a new way to look at combat modeling, i.e. the use of neuroscience-influenced computing (NIC) via the use of Hierarchical Temporal Memory (HTM) algorithms. Finally, some of the potential challenges of such an approach shall then be discussed to pave the way for further study.

2. DEFINING NETWORK-CENTRIC WARFARE As with most engineering design strategies, the first attempt to solve a problem is to use what has already been used to solve previous problems of a similar nature. The modeling of NCW has been no exception to this first-cut attempt at the understanding of a new phenomenon, whose understanding is crucial for an engineering application. However, as will be shown, the past has not been a convincingly suitable guide to the future of combat modeling, as combat has evolved into the future whilst its modeling has remained in the past.

Defining Network-Centric Warfare

In order to narrow the focus onto what differences have emerged in warfare, the discussion must now focus on what is exactly meant by network-centric warfare (NCW). The DoD describes NCW as “the combination of strategies, emerging tactics, techniques, and procedures, and organizations, that a fully or even a partially networked force can employ to create a decisive warfighting advantage” [1]. Such a description pinpoints immediately in what way the nature of warfare has changed from the past up to the present day: in particular, a partially networked force (e.g., Al Qaeda) using a combination of strategies, tactics, etc. can create a warfighting advantage on a grossly more sizable opponent (e.g., the U.S. military), which has its own strategies, tactics, etc. Obviously, it is this potential

for a warfighting advantage that is exploited to yield catastrophic events (e.g., the September 11th attacks) and therefore the mitigation of such an advantage is a desired result.

Exploiting Power of Network-Centric Warfare

Given that there is such an advantage offered by network-centric fighting, it is appropriate to inquire as to what are the elements that make its power, at times, so recognizable. Once again, the DoD points out, “the implementation of NCW is first of all about human behavior as opposed to information technology ... [thus,] when we examine ... the exploiting power of NCW, our focus should be on human behavior within the networked environment” [ibid]. So even though, NCW is an Information Age phenomenon, the source of its exploiting power is the networked human behavior, rather than the information technology through which the humans’ behavior is communicated. Nevertheless, even though human behavior is the primary feature of NCW, a biological approach to its understanding has been nearly completely absent in previous attempts to understand and, consequently, to predict it. Thus the aim of this work is to pinpoint what results from biology (specifically, neuroscience) can be of assistance in the modeling of NCW. Furthermore, since the networked nature of such human behavior has added another layer of complexity to combat modeling, thereby making Industrial Age combat models obsolete, such a new direction for combat modeling seems absolutely necessary to a successful understanding of NCW for both the present and, most certainly, the future.

3. MODELING NETWORK-CENTRIC WARFARE By first examining how past attempts to model NCW have shown their inability to do so, a new path will be opened as to what is the best way to understand and to react to the ever-shifting nature of this new style of asymmetric warfare.

Assumptions Underlying Combat Models

Starting with the context offered by the previous descriptions of NCW’s features and characteristics, Cares provides a survey of the problems that arise when NCW is represented in a computational framework [2]. In work for the Department of Defense, he identifies two classes of combat models: deterministic and stochastic models. In short, deterministic modeling fully describes all states of a system by a collection of closed-form equations, while in stochastic modeling the inclusion of randomness and uncertainty gives a dimension of unpredictability to otherwise analytical models. Furthermore, each approach has a set of certain assumptions embedded in their respective implementations that cause its results to be believed with severely constraining caveats.

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Deterministic Combat Modeling

Looking first at deterministic modeling, Cares notes that, since deterministic models prescribe a closed form rule set for a system’s behavior, “one cannot practically write an equation for every possible state or interaction that is observable in the process being modeled, so implicit representations necessarily abound” [ibid, pp. 2-3]. Furthermore, Cares notes that “aggregation” is the most common way that an implicit representation is done because “modelers assume complex interactions at time scales can be well represented at coarser scales by homogenizing behaviors and aggregating their effects.” In other words, due to the complexity of the dynamics within network-centric warfare (NCW), approximations are made so that the salient features of the problem in question can be represented in calculations.

In addition to the plaguing effects of implicit representation to a NCW combat model, the assumption of regularity in such deterministic models is also a common problem. When regularity is enforced on a model, small changes in the values of inputs cannot produce gross changes in the outcomes of that model. However, one of the striking features of NCW is that small changes in an adversary’s strategy, tactics or assets can produce a significant change in the outcome of its warfighting abilities. For example, today’s nuclear arms control efforts are focused on not letting an organization such as Al Qaeda obtain a nuclear weapon because such a ‘small change’ in this adversary’s assets can produce a significant change in its warfighting abilities.

Finally, command and control (C2) processes are rarely represented in deterministic models. Thus, in summary, the most notable assumptions of deterministic modeling are implicit representation, regularity and no C2 processes.

Stochastic Combat Modeling

Necessarily, stochastic models carry the same assumptions as deterministic models, i.e. implicit representation and regularity. However, on top of these faulty assumptions, in order to add an element of uncertainty to the modeling of a combat situation, Cares notes that in stochastic combat modeling “some input parameters must be random variables” [ibid]. Additionally, since the interactions between inputs are also uncertain, the parameters of a stochastic model are treated as independent variables. However, such ways of deciding how combat variables’ values change is contrary to the actual phenomena that occur in combat because chains of causality between given variables are actually at play; in other words, there is something else affecting a particular parameter’s value other than independent random chance. Cares agrees with this observation by noting, “complex chains of causality in the operational processes being modeled are considered inconsequential” [ibid] when the aforementioned assumptions are used for stochastic combat modeling.

Given this general overview of the assumptions that go into both deterministic and stochastic combat models, it is now necessary to survey some past efforts at combat modeling, with the emphasis being on how these models are not suitable tools with which to understand NCW.

Survey of Common Combat Models

From the earliest attempts at combat models during World War I, deterministic models were based on the idea of attrition. In attrition-based models, there are at least two sides in conflict with each other such that the rates at which each force diminishes the other determines which force or forces will emerge victorious, given that each force has an initial size. The most prevalent attrition model is the set of Lanchester equations [3], which represent force-on-force combat through a set of differential equations. Cares notes that these equations form “the basis for most of the current attrition-based combat simulations in use today” and he indicates examples of such. But, taking Joint Urban Operations (JUO) as an example of NCW, urban warfare is more complex than a force-on-force combat, which is assumed to occur independent of its setting, because the available assets and personnel of an urban environment necessarily contribute to warfare in such a terrain [4]. Therefore, there is already a problem with any attrition-based model that does not account for the impact of the environment in which warfare occurs.

Even though the bulk of deterministic modeling efforts are based on the use of the Lanchester equations, Cares also gives example of stochastic combat models that simulate combat via chains of independent events (e.g., The Naval Simulation System (NSS), Joint Warfare System (JWARS)). However, due to their use of the assumption that at least some events in combat are independent from each other, there is inherent fault in these models because in fact they have ignored the complex chains of causality connecting these events.

In tandem with the faulty attempts to model real-world causality with both deterministic and stochastic means, previous combat models have been made in light of the fact that “Industrial Age combat was characterized by limited communications, massed forces, and centralized command, control and decision making,” however today “military systems are increasingly characterized by dispersal of physical assets, information distribution and decentralized cognition” (ibid, p. 4). In short, a well-distributed network of assets over a small force can deliver a greater warfighting advantage today than a massed force with a less-distributed network of assets. Consequently, a valid combat model for such an environment must be built with the understanding that there is a fundamental difference between Industrial Age combat (i.e., one for which the Lanchester equations were a suitable approximation) and Information Age combat.

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Survey of Information Age Combat Modeling

It is quite clear by this point that there is a difference between how warfare is done today and how it was done in the past. To highlight this distinction, NCW is recognized as the “predominant theory of warfare in the Information Age” [ibid, p. 4], as opposed to the platform-centric warfare that characterized the Industrial Age. Nevertheless, today’s NCW modeling attempts are hindered by their reliance on attrition-based models, imprecise textual models or inaccurate analogies to other disciplines.

Cares notes several models for their inability to represent Information Age combat accurately, via their reliance on attrition-based models: Booz-Allen & Hamilton’s Entropy Based Warfare Model, which is a “traditional attrition-based model with Industrial Age assumptions” [5], a set of studies by RAND in which the model “[attaches] Information Age tuning parameters to what is essentially an Industrial Age model” [ibid Cares, p. 5] [6], and finally what is contained in Complexity Theory and Network-Centric Warfare by Moffat [7], which Cares faults for relying too much on “agent-based modeling and the RAND NCW research” [Cares, p. 5]. Thus the heritage from Industrial Age combat models hinders the attempts to model a completely different type of combat, i.e. one in an Information Age.

Textual models are another means of an attempt to model Information Age combat. The idea behind such an attempt is that a self-synchronized network will develop if a set of entities is given predefined rule sets. However, these rule sets are not necessary for the shared awareness that results from a synchronized network, and there are examples of such [8] [9]. Thus the rule sets are not necessary for the observed synchronization of a network, and so their use is an extra layer of complexity added onto an already complex problem, which is the understanding of NCW.

Finally, inaccurate analogies to the computing industry have also plagued Information Age combat modeling. However, the analogy seems tantalizingly relevant when comparing the most recent change in the information technology (IT) industry -- from platform-centric computing to network-centric computing -- to that of warfare -- from platform-centric combat to network-centric combat. But there are subtleties in NCW that make the analogy breakdown: Metcalf’s Law [10] states that a network’s power is proportional to its number of nodes, however such an observation does not hold for NCW and quite possibly to other networks. Nevertheless, the idea that computers can represent a network of humans is an idea that is crucial to the way proposed in this paper for understanding NCW, so such an idea will return later in this paper under a new, yet more credible, guise.

Faults with Current Information Age Combat Modeling

As has been shown, there are a slew of assumptions and issues of heritage that inhibit combat modeling from being unable to represent the complexities of NCW. Cares

summarizes these three reasons why the past is not a suitable guide for understanding the future of combat [Cares, p. 5]:

(1) The aggregation of fine-scaled behaviors as noise to a background level cannot accurately represent local elements’ massed effects or clever uses of information.

(2) Models that treat networked processes as independent events are invalid because the definition of a network is that its elements are interdependent.

(3) Networked performance exhibits irregularity but current modeling efforts enforce regularity.

Even though none of these reasons is directly aimed at one of the modeling approaches described in this paper, there are aspects of these reasons that appear in each approach. Considering the problems with past attempts at modeling Information Age combat, Cares concludes what is needed in the next generation of Information Age combat modeling [ibid, p. 5]:

An Information Age combat model will require a transformation in military modeling philosophy and must therefore address these challenges by properly representing complex local behaviors, explicitly representing interdependencies and capturing the skewed distribution of networked performance.

Therefore if the modeling is to represent combat in an Information Age in which “complex local behaviors” are crucial to its success then there must be “explicitly [represented] interdependencies,” from which it would be possible to “[capture] the skewed distribution of networked performance.” Furthermore, an understanding of the human elements that contribute to these interdependencies is necessary to understanding the complexity of NCW, and thus being able to engineer in its context.

4. EXAMPLE OF NETWORK-CENTRIC WARFARE One of the most pressing instances of NCW in U.S. military matters today is Joint Urban Operations (JUO). By first defining JUO and the central role of Battlespace Awareness (BA) in its execution, it will be clear at the end of this section how the role of human behavior is crucial to this example of network-centric warfare (NCW).

Defining Joint Urban Operations

On July 23, 2007, the Department of Defense (DoD) defined in The Joint Urban Operations (JUO) Joint Integrating Concept (JIC) the capabilities that are needed “to operate in an urban environment to defeat adversaries embedded and diffused within populated areas without causing catastrophic damage to the functioning of the society there” [11]. The 12 capabilities needed to perform JUO are [ibid, pp. 24-25]:

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1. The ability to collect, disseminate and access situational information on an urban system

2. The ability to assess an urban operational situation systematically

3. The ability to integrate all various elements of urban operations within the context of a theatre campaign

4. The ability to adapt operations to the changing situation

5. The ability to maneuver to, into, and through an urban area

6. The ability to apply highly discriminate destructive or disabling force to attack hostile elements while minimizing damage to an urban system

7. The ability to persuade municipal governments, organizations, and the general populace to cooperate with joint force operations

8. The ability to secure, control and protect urban areas to limit hostile presence, activity and influence

9. The ability to protect the joint force and other agencies and organizations within an urban area

10. The ability to selectively isolate all or relevant portions of an urban system to limit unwanted external influence

11. The ability before, during, and after combat operations to affect institutional and infrastructural improvements to strengthen selected urban subsystems identified as essential to the continued functioning of the urban system

12. The ability to facilitate humanitarian aid to suffering urban populations under both combat and non-combat conditions

Even though no particular capability must follow another in the sequence implied by their numbering, the 12 capabilities of the JUO are grouped into categories of Battlespace Awareness (JUO 001-002), Command and Control (JUO 003-004), Force Application (JUO 005-007), Force Protection (JUO 008-010) and Focused Logistics (JUO 011-012), all of which act simultaneously in JUO. In the capabilities required for JUO, the ability “to assess an urban operational situation systematically” is the key step from which any one of the other capabilities are determined; this ability is in the category of Battlespace Awareness (BA). For example, the adaptation of operations in a changing situation (JUO 004) depends on the en vivo assessment of the required adaptation. Or similarly, the ability to facilitate humanitarian aid (JUO 012) depends on the assessment of conditions, which can be either combat or non-combat, in which it is given. Thus, the role of assessment is central to the role of Battlespace Awareness (BA), and consequently, to that of Joint Urban Operations (JUO).

Battlespace Awareness in Joint Urban Operations

By identifying the central role of Battlespace Awareness (BA) in JUO, it is necesssary to analyze in-depth what is meant by the ability to “assess an urban operational situation systematically.” Thus, it is not only necessary to explain what is meant by an “urban operational situation” but it is also necessary to establish what is meant by its systematic “assessment.”

Urban Operational Situation—An urban operational situation arises out of the need to counteract warfare in an urban system. Generally speaking, an urban system “[comprises] a variety of subsystems that continuously input, throughput, and output various forms of matter, energy, and information” [ibid, p. 7]. Despite the generality of this definition, it is important to recognize that an evolution over time is instrumental to the workings of an urban system, as indicated by the “[continuous]” nature of its behavior. Similarly, considering another general definition, warfare is characterized by the presence of either “military operation between enemies” or “a struggle between competing entities” [12]. But when the concept of warfare is combined with that of an urban system, a new element adds to the resulting phenomenon’s complexity: the role of the civilians, i.e. the white agents, who simultaneously inhabit and rely on the features of the urban system to sustain their existence. The role that the white agents play in an urban operational situation is not only unique to each situation in which they exist but also crucial to the warfighting abilities of any force amidst a conflagration with either another force or set of forces. For example, the educational institutions and news entities are integral parts to the urban system because the white agents who inhabit the urban system use their products, but both of these entities can demonstrate a tremendous effect on urban warfare [13], and so their presence must be considered. Consequently, it is absolutely imperative to consider the goods and services that white agents provide to an urban system because without them, urban warfare would just be general warfare, i.e. a conflict between at least two opposing forces.

Assessment—With the description of an urban operational situation in place, it remains to clarify what is meant by the systematic “assessment” of such a situation in the definition of BA. The Joint Chiefs of Staff identifies it as a “continuous process that measures progress of the joint force toward mission accomplishment” [14]. Thus a “continuous,” i.e. a time-evolving process, occurs such that actions are taken and then observations are made to make a comparison between the current state and the original objective, i.e. the “mission.” In fact, the JUO JIC corroborates this role of assessment by noting, “systematic assessment is less a matter of reaching comprehensive truth about the urban system and more a matter of making assertions and hypotheses which are then revised through experience” [11, p. 15]. In short, assessment is the process whereby “assertions and hypotheses [are] revised through

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experience,” and furthermore its execution is central to the efficacy of Battlespace Awareness. With such an understanding of the role of BA, there is no doubt as to why the Joint Chiefs of Staff describes BA as follows [15]:

Battlespace Awareness is the situational knowledge whereby the Joint Force Commander plans operations and exercises command and control. It is the result of the processing and presentation of information comprehending the operational environment -- the status and dispositions of friendly, adversary, and non-aligned actors; and the impacts of physical, cultural, social, political, and economic factors on military operations.

Thus BA is “situational knowledge” that results from “processing ... [and] comprehending the operational environment,” while taking into account the additional factors (e.g., “physical, cultural,” etc.) that contribute to the difficulty in gaining that knowledge so that “command and control” can be exercised. Assessment of the joint force’s progress towards its mission can be done only once comprehension is reached, so that effective “command and control” can be exercised. Thus, assessment enables BA and, since BA is central to JUO, the role of “systematic assessment” is crucial to this demonstrative example of NCW.

5. HIERARCHICAL TEMPORAL MEMORY While it has been made clear that the role of human behavior is central both to the understanding of NCW and thus to its modeling, it is not yet clear what value such an insight has on the ability to find a solution for the problem of modeling network-centric warfare (NCW). Thus, this section will describe the use of machine learning algorithms called Hierarchical Temporal Memories (HTMs), with which it is believed that the features of NCW that have been intractable to previous attempts at its understanding will now be less so.

Background of HTMs

Hierarchical Temporal Memories (HTMs) are made on a platform built with the Python computing code; this platform is called the Numenta Platform for Intelligent Computing (NuPIC). In fact, HTMs are the computerization of ideas proposed by Jeff Hawkins in On Intelligence [16], which is a book that summarizes results gathered from the Redwood Neuroscience Institute’s work on the pivotal role of the cortex in the brain’s abilities. In short, the critical insight about the cortex in this work is that its function depends both on the hierarchical structure through which the information to which it is exposed must be processed and on its ability to recognize when events happen near one another in time, i.e. the temporal connection between events; with these two features of the cortex’s function, it is the contention of On Intelligence that memories are formed in such a way as to allow the breadth of human abilities of

which we are aware to occur everyday. While such a description of the impact on HTMs from neuroscience is for the moment sparse, a forthcoming analogy to the brain’s visual system shall lend additional credit to the validation of neuroscience’s use in the architecture of HTMs

Mechanics of HTMs

Without going into overwhelming detail about the inner workings of HTMs, a brief overview of its mechanics will show how its use can be instrumental for garnering an understanding of NCW. To begin, a network of nodes is established that will observe and extract an understanding of the data to which it is exposed. An example network is shown in Figure 1.

Figure 1: Example HTM Network

As the figure shows, the data is input at the bottom of the network so that the nodes above it can process it in such a way that a level of understanding is reached by the time the uppermost levels of the network are reached.

Comparison of HTM Networks to Processing in the Brain

Although a thorough detailing of the methods by which HTMs computationally enact the observed behavior of the human cortex is beyond the scope of this paper, a demonstrative parallel between the hierarchical processing of data in Figure 1 and the processing that occurs in the primate visual system shows, on the surface, how similar these two approaches to processing information are to one another. Figure 2 [17] shows the processing levels that are crucial to how a primate (e.g., a human) understands a stream of complex visual inputs. Just as in Figure 1, the flow of information is from bottom to top, where the visual input enters through the RGC, i.e. the retinal ganglion cells, and continues through the LGN, i.e. the lateral geniculate nucleus, up through the higher levels of visual processing. But the important feature of the primate cortex is that, as the information proceeds up the hierarchy, there are particular areas that recognize motion (e.g., MT, i.e. middle temporal), particular line orientations (e.g., V1 and V2, i.e. primary and secondary visual cortices), or even different faces (e.g., HC, i.e. hippocampal complex) [18], just to name a few features of visual stimuli. Furthermore, it is the claim of Felleman

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and Van Essen [17] that the architecture of information processing of the visual system applies for other systems of the brain (e.g., audio, tactile, etc.). Extending such an insight to become a general principle of the brain’s information processing capabilities, it is the goal of HTMs to extract structure from the world in the same way that the primate cortex does, i.e. recognizing specific high-level phenomena as information proceeds up the hierarchy; this is done via the spatial and temporal pooling algorithms embedded in the HTM network [20]. While such a comparison between the primate visual system and an HTM network is not intended to be a complete argument about the benefits of using HTMs to extract understanding from complex and time-evolving data, it is clear that the approach behind HTMs has grown out of a gross impact from neuroscience, and will continue to do so as the NuPIC algorithms are refined in the coming years. Consequently, noticing the neurobiological roots of HTMs in light of the presence of human behavior’s obvious impact on NCW, the choice obvious as to what direction a future understanding of asymmetric warfare, such as JUO, must take for satisfactory results; i.e., the modeling of asymmetric warfare must consider the neuroscience that is causing such warfare’s human-based phenomena to occur.

Figure 2: Processing of the Primate Visual Cortex

6. APPLYING HTMS TO NCW The final piece to the puzzle of understanding NCW in light of the gross impact that human behavior has on its outcome is to apply HTMs to its solution. Since HTMs are computer algorithms that have grown out of neuroscience’s findings about how brains process the complex and time-evolving worlds to which they are exposed, it seems to be a powerful extension of HTM algorithms to apply them to phenomena whose behavior is so intimately tied to that of human behavior. The specific case to which it shall be applied is Joint Urban Operations in Iraq after the regime of Saddam Hussein.

Dataset for HTM Application

An HTM is only as good as the data to which it is exposed. Thus, when picking this example application it is important to use a dataset that evolves over time, while tracking variables that give an overarching description of the complexities inherent to JUO. Then it is the aim of using the HTM to extract the high-level understanding from this data in the same way that a Subject Matter Expert (SME) would do the same. Therefore, a publicly available dataset provided by the Brookings Institution [19] has been selected as the dataset that will provide a suitable description of JUO’s time evolution. Furthermore, with such a dataset, it is now possible to attempt to understand NCW by applying HTMs to such a real world situation. Finally, as an indication of its propriety as a time-evolving description of JUO, the Brookings dataset gives quantitative data over 60 months on such environment variables as Iraqi civilian fatalities, the number of new U.S. troops to Baghdad, nationwide electricity, Iraqi public feeling that the country is heading in the right direction, and 30 other indicators of what the state of Iraq had been between 2003 and 2008.

Preliminary Results

As Figure 3 shows, preliminary results have already been generated by applying HTMs to the example case of JUO over the course of 5 years in post-Saddam Iraq.

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Figure 3: HTM Levels of Understanding By Node

At the bottom of the figure, the data is fed in, as indicated by the blue bars; this is equivalent to the lowest region in Figure 1. Then each node above a particular region of the inputs categorizes the inputs to which it is exposed; this is equivalent to the middle section of nodes indicated in Figure 1. After it categorizes the input to which it is exposed, each of these nodes passes its categorization up to the next node above it. Thus each node in the network receives time-evolving inputs of either categorization or raw data and then outputs another time-evolving categorization of that to which it has been exposed. In Figure 3, the categorization of each node is represented by the height of the colored curves shown in each node (as indicated by, e.g., “Level 1 [1]”, “Level 2 [2]”, etc.), where each color corresponds to a different categorization and its height corresponds to that node’s level of belief that it is seeing the category to which its color corresponds. Future Directions for HTMs in NCW

Even though the broad categorizing abilities of HTMs are exciting, especially as applied to this example case of JUO, there are many unknowns about this approach and before they are answered such an approach to understanding NCW remains justifiably questionable. Thus, some of the issues with the approach of using HTMs to understand NCW shall be discussed. An initial issue with using HTMs is that it is not known whether it is better to train the network on datasets that demonstrate obvious trends and then to test it on a complex one from the real world, or whether it is better to use the real world data for the training set and the data containing obvious trends for the test. Research is being done to determine which technique gives more accurate results. Additionally, another issue is the level of pre-processing that must take place with the data before an HTM is both trained and tested. For example, the results of Figure 3 are obtained after normalizing parts of the dataset so that the

magnitudes of certain variables’ values in the Brookings dataset would not mask the impact of variables with lower magnitudes. Necessarily, further research on this topic will be needed to ensure the most effective level of pre-processing for a given set of HTM algorithms. Looking at just these two issues, it is clear that there are open questions about using HTMs for understanding JUO, let alone NCW in general. But the answers to these questions, as well as the other ones not discussed in this paper, are the goal of current research and those results shall be forthcoming from the author.

7. CONCLUSIONS It has been shown that network-centric warfare (NCW) is the modus operandi by which warfare is carried out today in the world. With such a reality comes the need to represent that reality in a calculable fashion, so that decisions can effectively be made to ensure the proper course of action when a desired outcome is sought. The example of Joint Urban Operations (JUO) has been used as a test bed for the idea of using Hierarchical Temporal Memory (HTM) algorithms to understand the time-evolving and complex scenarios inherent to a phenomenon as varied and seemingly intractable as NCW. The decision to use HTMs for such an application has grown out of the obvious impact that human behavior has on the behavior of NCW; thus, using a neuroscience-influenced computing method such as HTM networks, which are nothing like the more commonly known artificial neural networks, seems to be an appropriate attempt to handle a problem as complex as NCW. While much research remains to be done regarding its applicability to the understanding of NCW, HTM networks remain a fertile ground for further investigation in light of the difficulties that preexisting combat models have in their ability to accurately represent network-centric and other forms of asymmetric warfare.

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BIOGRAPHY

Jason Sherwin is a PhD candidate in the Aerospace Systems Design Laboratory at the Georgia Institute of

Technology. In 2005, he earned his Bachelor of Arts from the University of Chicago in Physics and simultaneously completed the requirements for a music degree with a focus on composition. He continued his studies at Georgia Tech in 2005, completing his Master of Science in Aerospace Engineering the following fall semester. In the fall of 2008, he proposed his PhD topic, “A Methodology for Designing Complex Systems with Neuroscience-Influenced Computing.” In his research at Georgia Tech, he has worked on preliminary multi-disciplinary optimization of bimodal nuclear thermal rockets for Pratt & Whitney, design of the propulsion system of the Crew Exploration Vehicle for NASA, design of a planetary defense system from near-Earth objects and the effects of automation in automotive traffic patterns. A paper entitled “Towards an Intelligent Automotive System” details the latter work and has recently been accepted for refereed publication in the Journal of Intelligent Systems.

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