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Chapter 18. KNOWLEDGE ACQUISITION, REPRESENTATION, AND REASONING. Learning Objectives. Understand the nature of knowledge Understand the knowledge-engineering process Learn different approaches to knowledge acquisition Explain the pros and cons of different knowledge acquisition approaches

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Chapter 18

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Chapter 18

Chapter 18

KNOWLEDGE ACQUISITION, REPRESENTATION, AND REASONING


Learning objectives

Learning Objectives

  • Understand the nature of knowledge

  • Understand the knowledge-engineering process

  • Learn different approaches to knowledge acquisition

  • Explain the pros and cons of different knowledge acquisition approaches

  • Illustrate methods for knowledge verification and validation

  • Understand inference strategies in rule-based intelligent systems

  • Explain uncertainties and uncertainty processing in expert systems (ES)


Concepts of knowledge engineering

Concepts of Knowledge Engineering

  • Knowledge engineering

    The engineering discipline in which knowledge is integrated into computer systems to solve complex problems that normally require a high level of human expertise


Concepts of knowledge engineering1

Concepts of Knowledge Engineering

  • The knowledge-engineering process

    • Knowledge acquisition

    • Knowledge representation

    • Knowledge validation

    • Inferencing

    • Explanation and justification


Concepts of knowledge engineering2

Concepts of Knowledge Engineering

  • Knowledge representation

    A formalism for representing facts and rules in a computer about a subject or specialty

  • Knowledge validation (verification)

    The process of testing to determine whether the knowledge in an artificial intelligence system is correct and whether the system performs with an acceptable level of accuracy


Concepts of knowledge engineering3

Concepts of Knowledge Engineering


Concepts of knowledge engineering4

Concepts of Knowledge Engineering

  • CommonKADS

    The leading methodology to support structured knowledge engineering. It enables the recognition of opportunities and bottlenecks in how organizations develop, distribute, and apply their knowledge resources, and it is a tool for corporate knowledge management. CommonKADS provides the methods to perform a detailed analysis of knowledge intensive tasks and processes and supports the development of knowledge systems that support selected parts of the business process


The scope and types of knowledge

The Scope and Types of Knowledge

  • Documented knowledge

    For ES, stored knowledge sources not based directly on human expertise

  • Undocumented knowledge

    Knowledge that comes from sources that are not documented, such as human experts


The scope and types of knowledge1

The Scope and Types of Knowledge

  • Knowledge acquisition from databases

    • Many ES are constructed from knowledge extracted in whole or in part from databases

  • Knowledge acquisition via the Internet

    • The acquisition, availability, and management of knowledge via the Internet are becoming critical success issues for the construction and maintenance of knowledge-based systems


The scope and types of knowledge2

The Scope and Types of Knowledge

  • Levels of knowledge

    • Shallow knowledge

    • A representation of only surface level information that can be used to deal with very specific situations

    • Deep knowledge

    • A representation of information about the internal and causal structure of a system that considers the interactions among the system’s components


The scope and types of knowledge3

The Scope and Types of Knowledge


The scope and types of knowledge4

The Scope and Types of Knowledge

  • Major categories of knowledge

    • Declarative knowledge

      A representation of facts and assertions

    • Procedural knowledge

      Information about courses of action. Procedural knowledge contrasts with declarative knowledge

    • Metaknowledge

      In an expert system, knowledge about how the system operates or reasons. More generally, knowledge about knowledge


Methods of acquiring knowledge from experts

Methods of Acquiring Knowledge from Experts

  • Roles of knowledge engineers

    • Advise the expert on the process of interactive knowledge elicitation

    • Set up and appropriately manage the interactive knowledge acquisition tools

    • Edit the unencoded and coded knowledge base in collaboration with the expert

    • Set up and appropriately manage the knowledge-encoding tools

    • Validate application of the knowledge base in collaboration with the expert

    • Train clients in effective use of the knowledge base in collaboration with the expert by developing operational and training procedures


Methods of acquiring knowledge from experts1

Methods of Acquiring Knowledge from Experts


Methods of acquiring knowledge from experts2

Methods of Acquiring Knowledge from Experts

  • Elicitation of knowledge

    The act of extracting knowledge, generally automatically, from nonhuman sources; machine learning


Methods of acquiring knowledge from experts3

Methods of Acquiring Knowledge from Experts

  • Knowledge modeling methods

    • Manual method

      A human-intensive method for knowledge acquisition, such as interviews and observations, used to elicit knowledge from experts

    • Semiautomatic method

      A knowledge acquisition method that uses computer-based tools to support knowledge engineers in order to facilitate the process


Methods of acquiring knowledge from experts4

Methods of Acquiring Knowledge from Experts

  • Knowledge modeling methods

    • Automatic method

      An automatic knowledge acquisition method that involves using computer software to automatically discover knowledge from a set of data


Methods of acquiring knowledge from experts5

Methods of Acquiring Knowledge from Experts

  • Manual knowledge modeling methods

    • Interviews

      • Interview analysis

        An explicit, face-to-face knowledge acquisition technique that involves a direct dialog between the expert and the knowledge engineer

      • Walk-through

      • In knowledge engineering, a process whereby the expert walks (or talks) the knowledge engineer through the solution to a problem

      • Unstructured (informal) interview

        An informal interview that acquaints a knowledge engineer with an expert’s problem-solving domain


Methods of acquiring knowledge from experts6

Methods of Acquiring Knowledge from Experts

  • Manual knowledge modeling methods

    • Structured Interviews

      • A structured interview is a systematic, goal-oriented process

      • It forces organized communication between the knowledge engineer and the expert


Methods of acquiring knowledge from experts7

Methods of Acquiring Knowledge from Experts

  • Manual knowledge modeling methods

    • Process tracking

      The process of an expert system’s tracing the reasoning process in order to reach a conclusion

    • Protocol analysis

      A set of instructions governing the format and control of data in moving from one medium to another

    • Observations


Methods of acquiring knowledge from experts8

Methods of Acquiring Knowledge from Experts

  • Manual knowledge modeling methods

    • Other manual knowledge modeling methods

      • Case analysis

      • Critical incident analysis

      • Discussions with users

      • Commentaries

      • Conceptual graphs and models

      • Brainstorming

      • Prototyping

      • Multidimensional scaling

      • Johnson’s hierarchical clustering

      • Performance review


Methods of acquiring knowledge from experts9

Methods of Acquiring Knowledge from Experts

  • Manual knowledge modeling methods

    • Multidimensional scaling

      A method that identifies various dimensions of knowledge and then arranges them in the form of a distance matrix. It uses least-squares fitting regression to analyze, interpret, and integrate the data


Methods of acquiring knowledge from experts10

Methods of Acquiring Knowledge from Experts

  • Semiautomatic knowledge modeling methods

    • Repertory Grid Analysis (RGA)

      • Personal construct theory

        An approach in which each person is viewed as a “personal scientist” who seeks to predict and control events by forming theories, testing hypotheses, and analyzing results of experiments


Methods of acquiring knowledge from experts11

Methods of Acquiring Knowledge from Experts

  • Semiautomatic knowledge modeling methods

    • How RGA works

      • The expert identifies the important objects in the domain of expertise

      • The expert identifies the important attributes considered in making decisions in the domain

      • For each attribute, the expert is asked to establish a bipolar scale with distinguishable characteristics and their opposites

      • The interviewer picks any three of the objects and asks, “What attributes and traits distinguish any two of these objects from the third?” The answers are recorded in a grid

      • The grid can be used afterward to make recommendations in situations in which the importance of the attributes is known


Methods of acquiring knowledge from experts12

Methods of Acquiring Knowledge from Experts

  • Semiautomatic knowledge modeling methods

    • The use of RGA in ES

      • Expert transfer system (ETS)

        A computer program that interviews experts and helps them build expert systems

    • Card sorting data

    • Other computer-aided tools


Methods of acquiring knowledge from experts13

Methods of Acquiring Knowledge from Experts


Methods of acquiring knowledge from experts14

Methods of Acquiring Knowledge from Experts

  • Automatic knowledge modeling methods

    • The process of using computers to extract knowledge from data is called knowledge discovery

    • Two reasons for the use of automated knowledge acquisition:

      • Good knowledge engineers are highly paid and difficult to find

      • Domain experts are usually busy and sometimes uncooperative


Methods of acquiring knowledge from experts15

Methods of Acquiring Knowledge from Experts

  • Automatic knowledge modeling methods

    • Typical methods for knowledge discovery

      • Inductive learning

      • Neural computing

      • Genetic algorithms


Acquiring knowledge from multiple experts

Acquiring Knowledge from Multiple Experts

  • Major purposes of using multiple experts:

    • To better understand the knowledge domain

    • To improve knowledge-base validity, consistency, completeness, accuracy, and relevancy

    • To provide better productivity

    • To identify incorrect results more easily

    • To address broader domains

    • To be able to handle more complex problems and combine the strengths of different reasoning approaches


Acquiring knowledge from multiple experts1

Acquiring Knowledge from Multiple Experts

  • Multiple-expert scenarios

    • Individual experts

  • Primary and secondary experts

    • Small groups

    • Panels


Acquiring knowledge from multiple experts2

Acquiring Knowledge from Multiple Experts

  • Methods of handling multiple experts

    • Blend several lines of reasoning through consensus methods such as Delphi, nominal group technique (NGT), and group support systems (GSS)

    • Use an analytic approach, such as group probability or an

    • analytic hierarchy process

    • Keep the lines of reasoning distinct and select a specific line of reasoning based on the situation

    • Automate the process, using software or a blackboard approach.

    • Decompose the knowledge acquired into specialized knowledge sources


Automated knowledge acquisition from data and documents

Automated Knowledge Acquisition from Data and Documents

  • The objectives of using automated knowledge acquisition:

    • To increase the productivity of knowledge engineering (reduce the cost)

    • To reduce the skill level required from the knowledge engineer

    • To eliminate (or drastically reduce) the need for an expert

    • To eliminate (or drastically reduce) the need for a knowledge engineer

    • To increase the quality of the acquired knowledge


Automated knowledge acquisition from data and documents1

Automated Knowledge Acquisition from Data and Documents

  • Automated rule induction

    • Induction

      The process of reasoning from the specific to the general

    • Training set

      A set of data for inducing a knowledge model, such as a rule base or a neural network

    • Advantages of rule induction

      • Using rule induction allows ES to be used in more complicated and more commercially rewarding fields

      • The builder does not have to be a knowledge engineer


Automated knowledge acquisition from data and documents2

Automated Knowledge Acquisition from Data and Documents

  • Automated rule induction

    • Difficulties in implementing rule induction

      • Some induction programs may generate rules that are not easy for a human to understand

      • Rule induction programs do not select the attributes

      • The search process in rule induction is based on special algorithms that generate efficient decision trees, which reduce the number of questions that must be asked before a conclusion is reached


Automated knowledge acquisition from data and documents3

Automated Knowledge Acquisition from Data and Documents

  • Automated rule induction

    • Difficulties in implementing rule induction

      • Rule induction is only good for rule-based classification problems, especially of the yes/no type

      • The number of attributes must be fairly small

      • The number of examples necessary can be very large

      • The set of examples must be “sanitized”

      • Rule induction is limited to situations under certainty

      • The builder does not know in advance whether the number of examples is sufficient and whether the algorithm is good enough


Automated knowledge acquisition from data and documents4

Automated Knowledge Acquisition from Data and Documents

  • Interactive induction

    A computer-based means of knowledge acquisition that directly supports an expert in performing knowledge acquisition by guiding the expert through knowledge structuring


Knowledge verification and validation

Knowledge Verification and Validation

  • Knowledge acquired from experts needs to be evaluated for quality, including:

    • The main objective of evaluation is to assess an ES’s overall value

    • Validation is the part of evaluation that deals with the performance of the system

    • Verification is building the system right or substantiating that the system is correctly implemented to its specifications


Representation of knowledge

Representation of Knowledge

  • Production rule

    A knowledge representation method in which knowledge is formalized into rules that have IF parts and THEN parts (also called conditions and actions, respectively)


Representation of knowledge1

Representation of Knowledge

  • Inference rule (metarule)

    A rule that describes how other rules should be used or modified to direct an ES inference engine

  • Procedural rule

    A rule that advises on how to solve a problem, given that certain facts are known


Representation of knowledge2

Representation of Knowledge

  • Major advantages of rules

    • Rules are easy to understand

    • Inferences and explanations are easily derived

    • Modifications and maintenance are relatively easy

    • Uncertainty is easily combined with rules

    • Each rule is often independent of all others


Representation of knowledge3

Representation of Knowledge

  • Major limitations of rule representation:

    • Complex knowledge requires thousands of rules, which may create difficulties in using and maintaining the system

    • Builders like rules, so they try to force all knowledge into rules rather than look for more appropriate representations

    • Systems with many rules may have a search limitation in the control program

    • Some programs have difficulty evaluating rule-based systems and making inferences


Representation of knowledge4

Representation of Knowledge

  • Semantic network

    A knowledge representation method that consists of a network of nodes, representing concepts or objects, connected by arcs describing the relations between the nodes


Representation of knowledge5

Representation of Knowledge


Representation of knowledge6

Representation of Knowledge

  • Frame

    A knowledge representation scheme that associates one or more features with an object in terms of slots and particular slot values

    • Slot

      A sub-element of a frame of an object. A slot is a particular characteristic, specification, or definition used in forming a knowledge base

    • Facet

      An attribute or a feature that describes the content of a slot in a frame


Representation of knowledge7

Representation of Knowledge


Representation of knowledge8

Representation of Knowledge

  • Inheritance

    The process by which one object takes on or is assigned the characteristics of another object higher up in a hierarchy

  • Instantiate

    To assign (or substitute) a specific value or name to a variable in a frame (or in a logic expression), making it a particular “instance” of that variable


Representation of knowledge9

Representation of Knowledge


Representation of knowledge10

Representation of Knowledge


Representation of knowledge11

Representation of Knowledge

  • Decision table

    A table used to represent knowledge and prepare it for analysis


Chapter 18

Representation of Knowledge


Representation of knowledge12

Representation of Knowledge

  • Propositional logic (or calculus)

    A formal logical system of reasoning in which conclusions are drawn from a series of statements according to a strict set of rules

  • Predicate logic (or calculus)

    A logical system of reasoning used in artificial intelligence programs to indicate relationships among data items. It is the basis of the computer language PROLOG

  • PROLOG (programming in logic)

    A high-level computer language based on the concepts of predicate calculus


Representation of knowledge13

Representation of Knowledge


Reasoning in intelligent systems

Reasoning in Intelligent Systems

  • Commonsense reasoning

    The branch of artificial intelligence that is concerned with replicating human thinking

  • Reasoning in rule-based systems

    • Inference engine

      The part of an expert system that actually performs the reasoning function

    • Rule interpreter

      The inference mechanism in a rule-based system

    • Chunking

      A process of dividing and conquering, or dividing complex problems into subproblems


Reasoning in intelligent systems1

Reasoning in Intelligent Systems

  • Backward chaining

    A search technique that uses IF THEN rules and is used in production systems that begin with the action clause of a rule and works backward through a chain of rules in an attempt to find a verifiable set of condition clauses


Reasoning in intelligent systems2

Reasoning in Intelligent Systems

  • Forward chaining

    A data-driven search in a rule-based system


Reasoning in intelligent systems3

Reasoning in Intelligent Systems

  • Inference tree

    A schematic view of the inference process that shows the order in which rules are tested


Explanation and metaknowledge

Explanation and Metaknowledge

  • Explanation

    An attempt by an ES to clarify its reasoning, recommendations, or other actions (e.g., asking a question)

  • Explanation facility (justifier)

    The component of an expert system that can explain the system’s reasoning and justify its conclusions


Explanation and metaknowledge1

Explanation and Metaknowledge

  • Why explanations

  • How explanations

  • Other explanations

    • Who

    • What

    • Where

    • When

    • Why

    • How


Explanation and metaknowledge2

Explanation and Metaknowledge

  • Metaknowledge

    • Static explanation

      In an ES, an association of fixed explanation text with a rule to explain the rule’s meaning.

    • Dynamic explanation

      In ES, an explanation facility that reconstructs the reasons for its actions as it evaluates rules


Explanation and metaknowledge3

Explanation and Metaknowledge

  • Categorization of the explanation methods

    • Trace, or line of reasoning

    • Justification

    • Strategy


Inferencing with uncertainty

Inferencing with Uncertainty


Inferencing with uncertainty1

Inferencing with Uncertainty

  • The importance of uncertainty

    • Uncertainty is a serious problem

    • Avoiding it may not be the best strategy. Instead, we need to improve the methods for dealing with uncertainty


Inferencing with uncertainty2

Inferencing with Uncertainty

  • Representing uncertainty

    • Numeric representation

    • Graphic representation

    • Symbolic representation


Inferencing with uncertainty3

Inferencing with Uncertainty

  • Probabilities and related approaches

    • Probability ratio

    • Bayesian approach

      • Subjective probability

        A probability estimated by a manager without the benefit of a formal model

    • Dempster–Shafer theory of evidence

      • Belief function

        The representation of uncertainty without the need to specify exact probabilities


Inferencing with uncertainty4

Inferencing with Uncertainty

  • Theory of certainty factors

    • Certainty theory

      A framework for representing and working with degrees of belief of true and false in knowledge-based systems

    • Certainty factor (CF)

      A percentage supplied by an expert system that indicates the probability that the conclusion reached by the system is correct. Also, the degree of belief an expert has that a certain conclusion will occur if a certain premise is true

    • Disbelief

      The degree of belief that something is not going to happen


Inferencing with uncertainty5

Inferencing with Uncertainty

  • Theory of certainty factors

    • Combining certainty factors

      • Combining several certainty factors in one rule

      • Combining two or more rules


Expert systems development

Expert Systems Development


Expert systems development1

Expert Systems Development

  • Phase I: Project initialization

  • Phase II: System analysis and design

    • Conceptual design

    • Development strategy and methodology

    • Sources of knowledge


Expert systems development2

Expert Systems Development

  • Phase II: System analysis and design

    • Selection of the development environment

      • Expert system shell

        A computer program that facilitates relatively easy implementation of a specific expert system. Analogous to a DSS generator

      • Fifth-generation language (5GL)

        An artificial intelligence computer programming language. The best known are PROLOG and LISP

      • LISP (list processing)

        An artificial intelligence programming language, created by artificial intelligence pioneer John McCarthy, that is especially popular in the United States. It is based on manipulating lists of symbols


Expert systems development3

Expert Systems Development

  • Phase II: System analysis and design

    • Selection of the development environment

      • Tool kit

        A collection of related software items that assist a system developer

      • Domain-specific tool

        A software shell designed to be used only in the development of a specific area (e.g., a diagnostic system)


Expert systems development4

Expert Systems Development

  • Phase III: Rapid prototyping and the demonstration prototype

    • Demonstration prototype

      A small-scale prototype of a (usually expert) system that demonstrates some major capabilities of the final system on a rudimentary basis. It is used to gain support among users and managers

  • Phase IV: System development


Expert systems development5

Expert Systems Development

  • Phase V: Implementation

    • Acceptance by the user

    • Installation approaches and timing

    • Documentation and security

    • Integration and field testing


Expert systems development6

Expert Systems Development

  • Phase VI: Postimplemenatation

    • System operation

    • System maintenance

    • System expansion (upgrading)

    • System evaluation


Knowledge acquisition and the internet

Knowledge Acquisition and the Internet

  • The Internet as a communication medium

  • The Internet as an open knowledge source


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