chapter 18 knowledge acquisition, representation, and reasoning

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• 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• 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 Engineering• The knowledge-engineering process

1. Knowledge acquisition

2. Knowledge representation

3. Knowledge validation

4. Inferencing

5. Explanation and justification

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 Engineering

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• 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 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 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 Knowledge

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• Roles of knowledge engineers

1. Advise the expert on the process of interactive knowledge elicitation

2. Set up and appropriately manage the interactive knowledge acquisition tools

3. Edit the unencoded and coded knowledge base in collaboration with the expert

4. Set up and appropriately manage the knowledge-encoding tools

5. Validate application of the knowledge base in collaboration with the expert

6. 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 Experts

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 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 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 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


– Structured Interviews • A structured interview is a systematic, goal-

oriented process• It forces organized communication between the

knowledge engineer and the expert


– 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


– 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


– 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 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 Experts• Semiautomatic knowledge modeling methods

– How RGA works1. The expert identifies the important objects in the domain

of expertise2. The expert identifies the important attributes considered in

making decisions in the domain3. For each attribute, the expert is asked to establish a

bipolar scale with distinguishable characteristics and their opposites

4. 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

5. 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 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 Experts

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 Experts• Automatic knowledge modeling methods

– Typical methods for knowledge discovery• Inductive learning• Neural computing• Genetic algorithms

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 Experts• Multiple-expert scenarios

– Individual experts

• Primary and secondary experts– Small groups– Panels

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

• 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 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 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 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


• 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 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

• 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)


• 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


• 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


• 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


• 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


• 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


• 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


• Decision table

A table used to represent knowledge and prepare it for analysis


• 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

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 interpreterThe inference mechanism in a rule-based system

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

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 Systems• Forward chaining

A data-driven search in a rule-based system

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

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 Metaknowledge• Why explanations• How explanations• Other explanations

– Who – What– Where– When– Why – How

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 Metaknowledge• Categorization of the explanation

methods– Trace, or line of reasoning– Justification– Strategy

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


• Representing uncertainty– Numeric representation– Graphic representation– Symbolic representation


• 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


• 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


• Theory of certainty factors– Combining certainty factors

• Combining several certainty factors in one rule• Combining two or more rules

Expert Systems Development


• Phase I: Project initialization

• Phase II: System analysis and design– Conceptual design– Development strategy and methodology– Sources of knowledge


• 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


• 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)


• 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


• Phase V: Implementation– Acceptance by the user– Installation approaches and timing– Documentation and security– Integration and field testing


• Phase VI: Postimplemenatation– System operation– System maintenance– System expansion (upgrading)– System evaluation

Knowledge Acquisition and the Internet• The Internet as a communication medium

• The Internet as an open knowledge source

chapter 18 knowledge acquisition, representation, and reasoning

Documents

knowledge verification

management of knowledge

knowledge acquisitionexplain

maintenance of knowledge

knowledgeshallow knowledge

knowledge resources

stored knowledge sources

corporate knowledge