11

Artificial IntelligenceArtificial Intelligence

A.I. can be define as the artificial brain having A.I. can be define as the artificial brain having capability of thinking and understanding.capability of thinking and understanding.

A.I. is branch of computer science concerned with A.I. is branch of computer science concerned with the study and creation of computer system that exhibits the study and creation of computer system that exhibits some form of intelligence.some form of intelligence.

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Knowledge-based systemsKnowledge-based systems

33

Introduction, or what is knowledge?Introduction, or what is knowledge?Knowledge Knowledge can be defined as the body of facts and

principles accumulated by human kind or the act ,fact or state of knowing

is a theoretical or practical understanding of a subject The sum of what is currently known, and apparently

knowledge is power.

44

In biological organisms, knowledge is likely stored as complex In biological organisms, knowledge is likely stored as complex structure of interconnected Neurons.structure of interconnected Neurons.

In computers, knowledge is stored as symbolic structure but in In computers, knowledge is stored as symbolic structure but in form of collections of magnetic spots & voltage statesform of collections of magnetic spots & voltage states

55

Knowledge SourcesKnowledge SourcesDocumented (books, manuals, etc.)Documented (books, manuals, etc.)

Undocumented (in people's minds)Undocumented (in people's minds)From people, from machinesFrom people, from machines

Knowledge Acquisition from DatabasesKnowledge Acquisition from Databases

Knowledge Acquisition Via the InternetKnowledge Acquisition Via the Internet

66

Knowledge LevelsKnowledge Levels Shallow knowledge (surface) Shallow knowledge (surface) Deep knowledgeDeep knowledge Can implement a computerized representation that is Can implement a computerized representation that is

deeper than shallow knowledgedeeper than shallow knowledge Special knowledge representation methods (semantic Special knowledge representation methods (semantic

networks and frames) to allow the implementation of networks and frames) to allow the implementation of deeper-level reasoning (abstraction and analogy): deeper-level reasoning (abstraction and analogy): important expert activityimportant expert activity

Represent objects and processes of the domain of Represent objects and processes of the domain of expertise at this levelexpertise at this level

Relationships among objects are importantRelationships among objects are important

77

Scope of KnowledgeScope of Knowledge Knowledge acquisition is the extraction of knowledge from Knowledge acquisition is the extraction of knowledge from

sources of expertise and its transfer to the knowledge base and sources of expertise and its transfer to the knowledge base and sometimes to the inference enginesometimes to the inference engine

Knowledge is a collection of specialized facts, procedures and Knowledge is a collection of specialized facts, procedures and judgment rulesjudgment rules

88

Domain ExpertDomain Expert Those who possess knowledge are called experts.Those who possess knowledge are called experts.

Anyone can be considered a domain expert if he or she has deep Anyone can be considered a domain expert if he or she has deep knowledge (of both facts and rules) and strong practical experience in knowledge (of both facts and rules) and strong practical experience in a particular domain. The area of the domain may be limited. In a particular domain. The area of the domain may be limited. In general, an expert is a skilful person who can do things other people general, an expert is a skilful person who can do things other people cannot.cannot.

knowledgeable and skilled person capable of solving problems in a knowledgeable and skilled person capable of solving problems in a specific area or domain. specific area or domain. Has the greatest expertise in a given domain. Has the greatest expertise in a given domain. This expertise is to be captured in the expert system. This expertise is to be captured in the expert system. Therefore, the expert must be able to communicate his or her Therefore, the expert must be able to communicate his or her

knowledge, be willing to participate in the expert system knowledge, be willing to participate in the expert system development and commit a substantial amount of time to the development and commit a substantial amount of time to the project. project.

Most important player in the expert system development team.Most important player in the expert system development team.

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Major Categories of Major Categories of Knowledge Knowledge

Declarative KnowledgeDeclarative Knowledge

Procedural Knowledge Procedural Knowledge

Meta knowledgeMeta knowledge

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Declarative Knowledge Declarative Knowledge Descriptive Representation of KnowledgeDescriptive Representation of Knowledge

Expressed in a factual statementExpressed in a factual statement

Shallow Shallow

Important in the initial stage of knowledge acquisitionImportant in the initial stage of knowledge acquisition

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Procedural KnowledgeProcedural Knowledge Considers the manner in which things work under different Considers the manner in which things work under different

sets of circumstancessets of circumstances Includes step-by-step sequences and how-to types of Includes step-by-step sequences and how-to types of

instructionsinstructions May also include explanationsMay also include explanations Involves automatic response to stimuliInvolves automatic response to stimuli May tell how to use declarative knowledge and how to May tell how to use declarative knowledge and how to

make inferencesmake inferences

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Descriptive knowledgeDescriptive knowledge relates to a specific object. relates to a specific object. Includes information about the meaning, roles, Includes information about the meaning, roles, environment, resources, activities, associations and environment, resources, activities, associations and outcomes of the objectoutcomes of the object

Procedural knowledgeProcedural knowledge relates to the procedures employed relates to the procedures employed in the problem-solving processin the problem-solving process

1313

Meta knowledge Meta knowledge Knowledge about KnowledgeKnowledge about Knowledge

Meta knowledge can be simply defined as knowledge about Meta knowledge can be simply defined as knowledge about knowledge. knowledge.

Meta knowledge is knowledge about the use and control of Meta knowledge is knowledge about the use and control of domain knowledge in an expert system. domain knowledge in an expert system.

In ES, Meta knowledge refers to knowledge about the operation of In ES, Meta knowledge refers to knowledge about the operation of knowledge-based systemsknowledge-based systems

Its reasoning capabilitiesIts reasoning capabilities

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What’s in the knowledge base?What’s in the knowledge base? Facts about the specifics of the worldFacts about the specifics of the world

Northwestern is a private universityNorthwestern is a private universityThe first thing I did at the party was talk to John.The first thing I did at the party was talk to John.

Rules that describe ways to infer new facts from existing factsRules that describe ways to infer new facts from existing factsAll triangles have three sidesAll triangles have three sidesAll elephants are greyAll elephants are grey

Facts and rules are stated in a formal languageFacts and rules are stated in a formal languageGenerally some form of logic.Generally some form of logic.

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Knowledge-based systemsKnowledge-based systems A major turning point occurred in the field of AI with realization A major turning point occurred in the field of AI with realization

that “in knowledge lies the power”.that “in knowledge lies the power”.

This realization led to the development of a new class of This realization led to the development of a new class of system: i.e. knowledge –based system.system: i.e. knowledge –based system.

knowledge –based system get their power from the expert knowledge –based system get their power from the expert knowledge that has been coded into facts , rules & procedure.knowledge that has been coded into facts , rules & procedure.

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Components of KBSComponents of KBS

The knowledge is stored in a knowledge base separated from The knowledge is stored in a knowledge base separated from the control & inferencing component . This makes it possible the control & inferencing component . This makes it possible to add new knowledge or refine existing knowledge without to add new knowledge or refine existing knowledge without recompiling the control and inferencing programs. recompiling the control and inferencing programs.

Input output Input output unit unit

Inference control Inference control unitunit

Knowledge Knowledge basebase

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Structure and characteristics Structure and characteristics

AI programsAI programs::intelligenintelligent problem solving toolst problem solving tools

KBSsKBSsAI programs with special program AI programs with special program structure separated knowledge base structure separated knowledge base

ESsESsKBSs applied in a specific narrow fieldKBSs applied in a specific narrow field

AI programs

Knowledge-based systems

Expert systems

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The Knowledge HierarchyThe Knowledge Hierarchy

meta-knowledge

knowledgeinformation

datanoise

large volume, low value, usually no meaning/ context

lower volume, higher value, with context and associated meanings

understanding of a domain, can be applied

to solve problems

knowledge on knowledge(e.g how/when to apply)

may contain irrelevant items

which obscure data

managementinformation

systems

knowledge-based

systems

databases,transaction

systems

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Different type of knowledge base systemDifferent type of knowledge base system

2020

What is Knowledge Engineering?What is Knowledge Engineering?

the process of building an ESthe process of building an ES

the effort in developing a large quantity of effective knowledge the effort in developing a large quantity of effective knowledge (i.e. the KB)(i.e. the KB)

the acquisition of knowledge from a human expert or other the acquisition of knowledge from a human expert or other source (by a knowledge engineering) and its coding in the ESsource (by a knowledge engineering) and its coding in the ES

KE is important, because:KE is important, because:performance of an ES is largely determined by the quantity & performance of an ES is largely determined by the quantity &

quality of knowledge in its KBquality of knowledge in its KB

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knowledge Engineeringknowledge Engineering

The process of building knowledge-based systems is called The process of building knowledge-based systems is called knowledge engineering (KE). It has a great deal in common with knowledge engineering (KE). It has a great deal in common with software engineering, and is related to many computer science software engineering, and is related to many computer science domains such as artificial intelligence, databases, data mining, domains such as artificial intelligence, databases, data mining, expert systems, decision support systems and geographic expert systems, decision support systems and geographic information systems. Knowledge engineering is also related to information systems. Knowledge engineering is also related to mathematical logic and cognitive science as the knowledge is mathematical logic and cognitive science as the knowledge is produced by cognitive systems (mainly humans) and is produced by cognitive systems (mainly humans) and is structured by our understanding of how human reasoning or structured by our understanding of how human reasoning or logic works.logic works.

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Knowledge Engineering Knowledge Engineering Art of bringing the principles and tools of AI research to Art of bringing the principles and tools of AI research to

bear on difficult applications problems requiring experts' bear on difficult applications problems requiring experts' knowledge for their solutionsknowledge for their solutions

Technical issues of acquiring, representing and using Technical issues of acquiring, representing and using knowledge appropriately to construct and explain lines-of-knowledge appropriately to construct and explain lines-of-reasoningreasoning

Art of building complex computer programs that represent Art of building complex computer programs that represent and reason with knowledge of the world and reason with knowledge of the world

(Feigenbaum and McCorduck [1983]) (Feigenbaum and McCorduck [1983])

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The knowledge engineerThe knowledge engineer

someone who is capable of designing, building and testing someone who is capable of designing, building and testing an expert system. an expert system.

interviews the domain expert to find out how a particular interviews the domain expert to find out how a particular problem is solved. problem is solved.

establishes what reasoning methods the expert uses to establishes what reasoning methods the expert uses to handle facts and rules and decides how to represent them handle facts and rules and decides how to represent them in the expert system. in the expert system.

chooses some development software or an expert system chooses some development software or an expert system shell, or looks at programming languages for encoding the shell, or looks at programming languages for encoding the knowledge. knowledge.

responsible for testing, revising and integrating the expert responsible for testing, revising and integrating the expert system into the workplace.system into the workplace.

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Knowledge EngineeringKnowledge Engineering

Process of acquiring knowledge from experts and building Process of acquiring knowledge from experts and building knowledge baseknowledge baseNarrow perspectiveNarrow perspective

Knowledge acquisition, representation, validation, Knowledge acquisition, representation, validation, inference, maintenanceinference, maintenance

Broad perspectiveBroad perspectiveProcess of developing and maintaining intelligent systemProcess of developing and maintaining intelligent system

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Views of knowledge engineeringViews of knowledge engineering

There are two main views to knowledge engineering:There are two main views to knowledge engineering:

Transfer View – This is the traditional view. In this view, Transfer View – This is the traditional view. In this view, the assumption is to apply conventional knowledge the assumption is to apply conventional knowledge engineering techniques to transfer human knowledge into engineering techniques to transfer human knowledge into artificial intelligence systems. artificial intelligence systems.

Modeling View – This is the alternative view. In this view, Modeling View – This is the alternative view. In this view, the knowledge engineer attempts to model the knowledge the knowledge engineer attempts to model the knowledge and problem solving techniques of the domain expert into and problem solving techniques of the domain expert into the artificial intelligence system. the artificial intelligence system.

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Knowledge Engineering Process Knowledge Engineering Process ActivitiesActivities

Knowledge AcquisitionKnowledge Acquisition

Knowledge ValidationKnowledge Validation

Knowledge RepresentationKnowledge Representation

InferencingInferencing

Explanation and JustificationExplanation and Justification

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Knowledge Engineering Process Knowledge Engineering Process

Knowledgevalidation

(test cases)

KnowledgeRepresentation

KnowledgeAcquisition

Encoding

Inferencing

Sources of knowledge(experts, others)

Explanationjustification

Knowledgebase

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Knowledge Engineering in a NutshellKnowledge Engineering in a Nutshell

humanexpert

knowledgeengineer

knowledge base(in ES)

explicitknowledge

dialog

knowledgerefinement

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Phases of KEPhases of KE

Various phases of KE specific for the development of a knowledge-Various phases of KE specific for the development of a knowledge-based system:based system:

* Assessment of the problem* Assessment of the problem* Acquisition and structuring of related information, * Acquisition and structuring of related information, knowledge and specific preferencesknowledge and specific preferences* Development of a knowledge-based system shell/structure* Development of a knowledge-based system shell/structure* Implementation of the structured knowledge into * Implementation of the structured knowledge into knowledge-basesknowledge-bases* Testing and validation of the inserted knowledge* Testing and validation of the inserted knowledge* Integration and maintenance of the system* Integration and maintenance of the system* Revision and evaluation of the system."* Revision and evaluation of the system."

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Knowledge Engineering PrinciplesKnowledge Engineering Principles

Knowledge engineers acknowledge that there are different types of knowledge, Knowledge engineers acknowledge that there are different types of knowledge, and that the right approach and technique should be used for the knowledge and that the right approach and technique should be used for the knowledge required. required.

Knowledge engineers acknowledge that there are different types of experts and Knowledge engineers acknowledge that there are different types of experts and expertise, such that methods should be chosen appropriately. expertise, such that methods should be chosen appropriately.

Knowledge engineers recognize that there are different ways of representing Knowledge engineers recognize that there are different ways of representing knowledge, which can aid the acquisition, validation and re-use of knowledge. knowledge, which can aid the acquisition, validation and re-use of knowledge.

Knowledge engineers recognize that there are different ways of using Knowledge engineers recognize that there are different ways of using knowledge, so that the acquisition process can be guided by the project aims. knowledge, so that the acquisition process can be guided by the project aims.

Knowledge engineers use structured methods to increase the efficiency of the Knowledge engineers use structured methods to increase the efficiency of the acquisition processacquisition process

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The main players in the development teamThe main players in the development team

Expert System

End-user

Knowledge Engineer ProgrammerDomain Expert

Project Manager

Expert SystemDevelopment Team

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Intelligent SystemIntelligent SystemA System can be constructed as a intelligence system if it has four A System can be constructed as a intelligence system if it has four major techniques of knowledge representation.major techniques of knowledge representation.

1.Logic1.Logic The logic is a formal procedure because of which implications are The logic is a formal procedure because of which implications are created from the set of known facts.created from the set of known facts.

2.Production Systems2.Production Systems The production systems studies the new facts and the known The production systems studies the new facts and the known facts and finds the desired conclusion.facts and finds the desired conclusion.

3.Semantic networks3.Semantic networks It is a network of symbols that describe relationship between It is a network of symbols that describe relationship between elements of knowledgeelements of knowledge

4.Frames4.Frames These are the data structures which consists of expectations for a These are the data structures which consists of expectations for a given situation.given situation.

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““Although knowledge representation is one Although knowledge representation is one of the central and in some ways most of the central and in some ways most familiar concepts in AI, the most familiar concepts in AI, the most fundamental question about itfundamental question about it

What is it?What is it? has rarely been answered directly.”has rarely been answered directly.”

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What is a knowledge representation?What is a knowledge representation?

A knowledge representation (KR) is most fundamentally a surrogate, a substitute for A knowledge representation (KR) is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather the thing itself, used to enable an entity to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it. than acting, i.e., by reasoning about the world rather than taking action in it.

It is a set of ontological commitments, i.e., an answer to the question: In what terms It is a set of ontological commitments, i.e., an answer to the question: In what terms should I think about the world? should I think about the world?

It is a fragmentary theory of intelligent reasoning, expressed in terms of three It is a fragmentary theory of intelligent reasoning, expressed in terms of three components: (i) the representation's fundamental conception of intelligent reasoning; components: (i) the representation's fundamental conception of intelligent reasoning; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends. recommends.

It is a medium for pragmatically efficient computation, i.e., the computational It is a medium for pragmatically efficient computation, i.e., the computational environment in which thinking is accomplished. One contribution to this pragmatic environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance a representation provides for organizing efficiency is supplied by the guidance a representation provides for organizing information so as to facilitate making the recommended inferences. information so as to facilitate making the recommended inferences.

It is a medium of human expression, i.e., a language in which we say things about the It is a medium of human expression, i.e., a language in which we say things about the world. world.

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Elements of a RepresentationElements of a Representation

Represented world: about what? Represented world: about what?

Representing world: using what? Representing world: using what?

Representing rules: how to map? Representing rules: how to map?

Process that uses the representation: conventions and systems Process that uses the representation: conventions and systems that use the representations resulting from above. that use the representations resulting from above.

Analog versus SymbolicAnalog versus Symbolic

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Understanding the roles and acknowledging their diversity has several useful Understanding the roles and acknowledging their diversity has several useful consequences. First, each role requires something slightly different from a consequences. First, each role requires something slightly different from a representation; each accordingly leads to an interesting and different set of representation; each accordingly leads to an interesting and different set of properties we want a representation to have. properties we want a representation to have.

Second, we believe the roles provide a framework useful for characterizing a Second, we believe the roles provide a framework useful for characterizing a wide variety of representations. We suggest that the fundamental "mindset" of wide variety of representations. We suggest that the fundamental "mindset" of a representation can be captured by understanding how it views each of the a representation can be captured by understanding how it views each of the roles, and that doing so reveals essential similarities and differences. roles, and that doing so reveals essential similarities and differences.

Third, we believe that some previous disagreements about representation are Third, we believe that some previous disagreements about representation are usefully disentangled when all five roles are given appropriate consideration. usefully disentangled when all five roles are given appropriate consideration. We demonstrate this by revisiting and dissecting the early arguments We demonstrate this by revisiting and dissecting the early arguments concerning frames and logic. concerning frames and logic.

Finally, we believe that viewing representations in this way has consequences Finally, we believe that viewing representations in this way has consequences for both research and practice. For research, this view provides one direct for both research and practice. For research, this view provides one direct answer to a question of fundamental significance in the field. It also suggests answer to a question of fundamental significance in the field. It also suggests adopting a broad perspective on what's important about a representation, and it adopting a broad perspective on what's important about a representation, and it makes the case that one significant part of the representation endeavor--makes the case that one significant part of the representation endeavor--capturing and representing the richness of the natural world--is receiving capturing and representing the richness of the natural world--is receiving insufficient attention. insufficient attention.

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TerminologyTerminology

Two points of terminology will assist in our presentation. First, Two points of terminology will assist in our presentation. First, we use the term inference in a generic sense, to mean any we use the term inference in a generic sense, to mean any way to get new expressions from old. We are only rarely way to get new expressions from old. We are only rarely talking about sound logical inference and when doing so refer talking about sound logical inference and when doing so refer to that explicitly. to that explicitly.

Second, to give them a single collective name, we refer to the Second, to give them a single collective name, we refer to the familiar set of basic representation tools like logic, rules, familiar set of basic representation tools like logic, rules, frames, semantic nets, etc., as knowledge representation frames, semantic nets, etc., as knowledge representation technologies. technologies.

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We have argued that a knowledge representation plays five distinct roles, We have argued that a knowledge representation plays five distinct roles, each important to the nature of representation and its basic tasks. Those each important to the nature of representation and its basic tasks. Those roles create multiple, sometimes competing demands, requiring selective roles create multiple, sometimes competing demands, requiring selective and intelligent tradeoff among the desired characteristics. Those five roles and intelligent tradeoff among the desired characteristics. Those five roles also aid in characterizing clearly the spirit of representations and also aid in characterizing clearly the spirit of representations and representation technologies that have been developed. representation technologies that have been developed.

This view has consequences for both research and practice in the field. On This view has consequences for both research and practice in the field. On the research front it argues for a conception of representation broader than the research front it argues for a conception of representation broader than the one often used, urging that all of the five aspects are essential the one often used, urging that all of the five aspects are essential representation issues. It argues that the ontological commitment a representation issues. It argues that the ontological commitment a representation supplies is one of its most significant contributions; hence the representation supplies is one of its most significant contributions; hence the commitment should be both substantial and carefully chosen. It also commitment should be both substantial and carefully chosen. It also suggests that the fundamental task of representation is describing the suggests that the fundamental task of representation is describing the natural world and claims that the field would advance furthest by taking this natural world and claims that the field would advance furthest by taking this as its central preoccupation. as its central preoccupation.

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Different levels of knowledge Different levels of knowledge representationrepresentation

Mental ImageMental Image

Written TextWritten Text

Magnetic SpotsMagnetic Spots

Binary NumbersBinary Numbers

Character StringsCharacter Strings

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How Knowledge Representation WorksHow Knowledge Representation Works

Intelligence requires knowledgeIntelligence requires knowledge Computational models of intelligence require models of Computational models of intelligence require models of

knowledgeknowledge Use formalisms to write down knowledgeUse formalisms to write down knowledge

Expressive enough to capture human knowledgeExpressive enough to capture human knowledgePrecise enough to be understood by machinesPrecise enough to be understood by machines

Separate knowledge from computational mechanisms that Separate knowledge from computational mechanisms that process itprocess itImportant part of cognitive model is what the organism Important part of cognitive model is what the organism

knows.knows.

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How knowledge representations are How knowledge representations are used in cognitive modelsused in cognitive models

Contents of KB is Contents of KB is part of cognitive part of cognitive modelmodel

Some models Some models hypothesize hypothesize multiple knowledge multiple knowledge bases.bases.

KnowledgeBase

InferenceMechanism(s)

LearningMechanism(s)

Examples,Statements

Questions,requests

Answers,analyses

4242

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Knowledge acquisitionKnowledge acquisition Knowledge acquisition includes the elicitation, Knowledge acquisition includes the elicitation,

collection, analysis, modelling and validation of collection, analysis, modelling and validation of knowledge for knowledge for knowledge engineeringknowledge engineering and and knowledge managementknowledge management projects projects

4444

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Issues in Knowledge AcquisitionIssues in Knowledge Acquisition

. Some of the most important issues in knowledge acquisition are as . Some of the most important issues in knowledge acquisition are as follows:follows:

Most knowledge is in the heads of experts Most knowledge is in the heads of experts

Experts have vast amounts of knowledge Experts have vast amounts of knowledge

Experts have a lot of tacit knowledge Experts have a lot of tacit knowledge They don't know all that they know and use They don't know all that they know and use Tacit knowledge is hard (impossible) to describe Tacit knowledge is hard (impossible) to describe

Experts are very busy and valuable people Experts are very busy and valuable people

Each expert doesn't know everything Each expert doesn't know everything

Knowledge has a "shelf life"Knowledge has a "shelf life"

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Requirements for KA TechniquesRequirements for KA Techniques Because of these issues, techniques are required Because of these issues, techniques are required

which:which: Take experts off the job for short time periods Take experts off the job for short time periods Allow non-experts to understand the knowledge Allow non-experts to understand the knowledge Focus on the essential knowledge Focus on the essential knowledge Can capture tacit knowledge Can capture tacit knowledge Allow knowledge to be collated from different Allow knowledge to be collated from different

experts experts Allow knowledge to be validated and maintainedAllow knowledge to be validated and maintained

4747

KA Techniques …KA Techniques … Many techniques have been developed to help elicit knowledge from an expert. Many techniques have been developed to help elicit knowledge from an expert.

These are referred to as knowledge elicitation or knowledge acquisition (KA) These are referred to as knowledge elicitation or knowledge acquisition (KA) techniques. The term "KA techniques" is commonly used.The following list techniques. The term "KA techniques" is commonly used.The following list gives a brief introduction to the types of techniques used for acquiring, gives a brief introduction to the types of techniques used for acquiring, analysing and modelling knowledge:analysing and modelling knowledge:

Protocol-generation techniquesProtocol-generation techniques include various types of interviews (unstructured, include various types of interviews (unstructured, semi-structured and structured), reporting techniques (such as self-report and semi-structured and structured), reporting techniques (such as self-report and shadowing) and observational techniques shadowing) and observational techniques

Protocol analysis techniquesProtocol analysis techniques are used with transcripts of interviews or other text- are used with transcripts of interviews or other text-based information to identify various types of knowledge, such as goals, based information to identify various types of knowledge, such as goals, decisions, relationships and attributes. This acts as a bridge between the use of decisions, relationships and attributes. This acts as a bridge between the use of protocol-based techniques and knowledge modelling techniques. protocol-based techniques and knowledge modelling techniques.

Hierarchy-generation techniques, such as Hierarchy-generation techniques, such as ladderingladdering, are used to build , are used to build taxonomies or other hierarchical structures such as goal trees and decision taxonomies or other hierarchical structures such as goal trees and decision networks. networks.

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KA Techniques …KA Techniques … Matrix-based techniquesMatrix-based techniques involve the construction of grids indicating such things as involve the construction of grids indicating such things as

problems encountered against possible solutions. Important types include the use problems encountered against possible solutions. Important types include the use of frames for representing the properties of concepts and the of frames for representing the properties of concepts and the repertory grid techniquerepertory grid technique used to elicit, rate, analyse and categorise the properties of used to elicit, rate, analyse and categorise the properties of concepts. concepts.

Sorting techniquesSorting techniques are used for capturing the way people compare and order are used for capturing the way people compare and order concepts, and can lead to the revelation of knowledge about classes, properties concepts, and can lead to the revelation of knowledge about classes, properties and priorities. and priorities.

Limited-information and constrained-processing tasksLimited-information and constrained-processing tasks are techniques that either limit are techniques that either limit the time and/or information available to the expert when performing tasks. For the time and/or information available to the expert when performing tasks. For instance, the twenty-questions technique provides an efficient way of accessing instance, the twenty-questions technique provides an efficient way of accessing the key information in a domain in a prioritised order. the key information in a domain in a prioritised order.

Diagram-based techniquesDiagram-based techniques include the generation and use of concept maps, state include the generation and use of concept maps, state transition networks, event diagrams and process maps. The use of these is transition networks, event diagrams and process maps. The use of these is particularly important in capturing the "what, how, when, who and why" of tasks particularly important in capturing the "what, how, when, who and why" of tasks and events. and events.

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Knowledge Acquisition Methods: Knowledge Acquisition Methods: An OverviewAn Overview

Manual Manual

SemiautomaticSemiautomatic

Automatic (Computer Aided)Automatic (Computer Aided)

5050

Manual Methods Manual Methods Structured AroundStructured Around

InterviewsInterviews ProcessProcess

InterviewingInterviewing

Tracking the Reasoning Process Tracking the Reasoning Process

ObservingObserving

Manual methods: slow, expensive and sometimes Manual methods: slow, expensive and sometimes inaccurateinaccurate

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Manual Methods of knowledge AcquisitionManual Methods of knowledge Acquisition

Elicitation

Knowledgebase

Documentedknowledge

Experts

CodingKnowledgeengineer

5252

Semiautomatic MethodsSemiautomatic Methods

Support Experts Directly Support Experts Directly

Help Knowledge EngineersHelp Knowledge Engineers

5353

Expert-Driven Expert-Driven Knowledge AcquisitionKnowledge Acquisition

Knowledgebase

Knowledgeengineer

Expert CodingComputer-aided(interactive)interviewing

5454

Automatic MethodsAutomatic Methods

Expert’s and/or the knowledge engineer’s roles are Expert’s and/or the knowledge engineer’s roles are minimized (or eliminated) minimized (or eliminated)

Induction Method.Induction Method.

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Induction-Driven Knowledge AcquisitionInduction-Driven Knowledge Acquisition

Knowledgebase

Case historiesand examples

Inductionsystem

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5757

5858

5959

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Knowledge Acquisition DifficultiesKnowledge Acquisition Difficulties

Problems in Transferring KnowledgeProblems in Transferring Knowledge

Expressing KnowledgeExpressing Knowledge

Transfer to a MachineTransfer to a Machine

Number of ParticipantsNumber of Participants

Structuring KnowledgeStructuring Knowledge

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Experts may lack time or not cooperateExperts may lack time or not cooperate

Testing and refining knowledge is complicatedTesting and refining knowledge is complicated

Poorly defined methods for knowledge elicitationPoorly defined methods for knowledge elicitation

System builders may collect knowledge from one source, but the System builders may collect knowledge from one source, but the relevant relevant

knowledge may be scattered across several sourcesknowledge may be scattered across several sources

May collect documented knowledge rather than use expertsMay collect documented knowledge rather than use experts

The knowledge collected may be incompleteThe knowledge collected may be incomplete

Difficult to recognize specific knowledge when mixed with irrelevant Difficult to recognize specific knowledge when mixed with irrelevant datadata

Other ReasonsOther Reasons

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Experts may change their behavior when observed and/or Experts may change their behavior when observed and/or interviewedinterviewed

Problematic interpersonal communication between the Problematic interpersonal communication between the knowledge engineer and the expertknowledge engineer and the expert

CriticalCritical The ability and personality of the knowledge engineer The ability and personality of the knowledge engineer Must develop a positive relationship with the expertMust develop a positive relationship with the expert The knowledge engineer must create the right impressionThe knowledge engineer must create the right impression

Computer-aided knowledge acquisition tools Computer-aided knowledge acquisition tools

Extensive integration of the acquisition effortsExtensive integration of the acquisition efforts

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Advantages of KBSs and ESsAdvantages of KBSs and ESs

make up for shortage of experts, spread expert’ knowledge on available price make up for shortage of experts, spread expert’ knowledge on available price

field of interest’ changes are well-tracked field of interest’ changes are well-tracked

increaseincrease expert’ ability and efficiencyexpert’ ability and efficiency

preserve know-howpreserve know-how

can be developed systems unrealizabled with tradicional technology can be developed systems unrealizabled with tradicional technology (Buck (Buck Rogers)Rogers)

self-consistents in advising, self-consistents in advising, equable in performance are available equable in performance are available permanently permanently

able to work even with partial, non-complete data able to work even with partial, non-complete data

able to give expanation able to give expanation

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Disadvantages of KBSs and ESsDisadvantages of KBSs and ESs

their knowledge is from a narrow field, don’t know the limits their knowledge is from a narrow field, don’t know the limits

the answers are not always correct (advices have to be the answers are not always correct (advices have to be analysedanalysed!)!)

don’t have common sence don’t have common sence ((greatest restriction) greatest restriction) all of the all of the self-evident checking have to be definedself-evident checking have to be defined

((many exceptions many exceptions increase the size of KB and the running increase the size of KB and the running timetime))

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Role V: A KR is a Medium of Human ExpressionRole V: A KR is a Medium of Human Expression

Finally, knowledge representations are also the means by Finally, knowledge representations are also the means by which we express things about the world, the medium of which we express things about the world, the medium of expression and communication in which we tell the machine expression and communication in which we tell the machine (and perhaps one another) about the world. This role for (and perhaps one another) about the world. This role for representations is inevitable so long as we need to tell the representations is inevitable so long as we need to tell the machine (or other people) about the world, and so long as we machine (or other people) about the world, and so long as we do so by creating and communicating representations. do so by creating and communicating representations. (5)(5) The The fifth role for knowledge representations is thus as a medium of fifth role for knowledge representations is thus as a medium of expression and communication for use by us. expression and communication for use by us.

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Role IV: A KR is a Medium for Role IV: A KR is a Medium for Efficient ComputationEfficient Computation

From a purely mechanistic view, reasoning in From a purely mechanistic view, reasoning in machines (and somewhat more debatably, in people) machines (and somewhat more debatably, in people) is a computational process. Simply put, to use a is a computational process. Simply put, to use a representation we must compute with it. As a result, representation we must compute with it. As a result, questions about computational efficiency are inevitably questions about computational efficiency are inevitably central to the notion of representation. central to the notion of representation.

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Role III: A KR is a Fragmentary Theory Of Intelligent ReasoningRole III: A KR is a Fragmentary Theory Of Intelligent Reasoning

The third role for a representation is as a fragmentary theory of The third role for a representation is as a fragmentary theory of intelligent reasoning. This role comes about because the initial intelligent reasoning. This role comes about because the initial conception of a representation is typically motivated by some conception of a representation is typically motivated by some insight indicating how people reason intelligently, or by some insight indicating how people reason intelligently, or by some belief about what it means to reason intelligently at all. belief about what it means to reason intelligently at all.

The theory is fragmentary in two distinct senses: (i) the The theory is fragmentary in two distinct senses: (i) the representation typically incorporates only part of the insight or representation typically incorporates only part of the insight or belief that motivated it, and (ii) that insight or belief is in turn only belief that motivated it, and (ii) that insight or belief is in turn only a part of the complex and multi-faceted phenomenon of a part of the complex and multi-faceted phenomenon of intelligent reasoning. intelligent reasoning.

A representation's theory of intelligent reasoning is often implicit, A representation's theory of intelligent reasoning is often implicit, but can be made more evident by examining its three but can be made more evident by examining its three components: (i) the representation's fundamental conception of components: (i) the representation's fundamental conception of intelligent inference; (ii) the set of inferences the representation intelligent inference; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends. sanctions; and (iii) the set of inferences it recommends.

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Role II: A KR is a Set of Ontological CommitmentsRole II: A KR is a Set of Ontological Commitments

If, as we have argued, all representations are imperfect If, as we have argued, all representations are imperfect approximations to reality, each approximation attending to approximations to reality, each approximation attending to some things and ignoring others, then in selecting any some things and ignoring others, then in selecting any representation we are in the very same act unavoidably making representation we are in the very same act unavoidably making a set of decisions about how and what to see in the world. That a set of decisions about how and what to see in the world. That is, selecting a representation means making a set of is, selecting a representation means making a set of ontological commitments. ontological commitments. (2)(2) The commitments are in effect a The commitments are in effect a strong pair of glasses that determine what we can see, bringing strong pair of glasses that determine what we can see, bringing some part of the world into sharp focus, at the expense of blurring some part of the world into sharp focus, at the expense of blurring other parts. other parts.

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Role I: A KR is a SurrogateRole I: A KR is a Surrogate

Any intelligent entity that wishes to reason about its world Any intelligent entity that wishes to reason about its world encounters an important, inescapable fact: reasoning is a encounters an important, inescapable fact: reasoning is a process that goes on internally, while most things it wishes to process that goes on internally, while most things it wishes to reason about exist only externally. A program (or person) reason about exist only externally. A program (or person) engaged in planning the assembly of a bicycle, for instance, engaged in planning the assembly of a bicycle, for instance, may have to reason about entities like wheels, chains, may have to reason about entities like wheels, chains, sprockets, handle bars, etc., yet such things exist only in the sprockets, handle bars, etc., yet such things exist only in the external world. external world.

This unavoidable dichotomy is a fundamental rationale and role This unavoidable dichotomy is a fundamental rationale and role for a representation: it functions as a surrogate inside the for a representation: it functions as a surrogate inside the reasoner, a stand-in for the things that exist in the world. reasoner, a stand-in for the things that exist in the world. Operations on and with representations substitute for operations Operations on and with representations substitute for operations on the real thing, i.e., substitute for direct interaction with the on the real thing, i.e., substitute for direct interaction with the world. In this view reasoning itself is in part a surrogate for world. In this view reasoning itself is in part a surrogate for action in the world, when we can not or do not (yet) want to take action in the world, when we can not or do not (yet) want to take that action. that action. (1)(1)