KNOWLEDGE ACQUISITION, REPRESENTATION, AND REASONING

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Chapter 18

• KNOWLEDGE ACQUISITION, REPRESENTATION, AND
  REASONING

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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)

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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

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Concepts of Knowledge Engineering

• The knowledge-engineering process
• Knowledge acquisition
• Knowledge representation
• Knowledge validation
• Inferencing
• Explanation and justification

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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

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Concepts of Knowledge Engineering
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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

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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

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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

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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 systems
  components

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The Scope and Types of Knowledge
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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

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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

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Methods of Acquiring Knowledge from Experts
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Methods of Acquiring Knowledge from Experts

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

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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

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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

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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 experts problem-solving domain

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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

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Methods of Acquiring Knowledge from Experts

• Manual knowledge modeling methods
• Process tracking
• The process of an expert systems 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

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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
• Johnsons hierarchical clustering
• Performance review

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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

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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

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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

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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

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Methods of Acquiring Knowledge from Experts
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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

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Methods of Acquiring Knowledge from Experts

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

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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

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Acquiring Knowledge from Multiple Experts

• Multiple-expert scenarios
• Individual experts
• Primary and secondary experts
• Small groups
• Panels

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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

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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

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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

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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

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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

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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

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Knowledge Verification and Validation

• Knowledge acquired from experts needs to be
  evaluated for quality, including
• The main objective of evaluation is to assess an
  ESs 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

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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)

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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

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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

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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

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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

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Representation of Knowledge
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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

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Representation of Knowledge
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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

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Representation of Knowledge
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Representation of Knowledge
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Representation of Knowledge

• Decision table
• A table used to represent knowledge and prepare
  it for analysis

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Representation of Knowledge
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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

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Representation of Knowledge
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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

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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

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Reasoning in Intelligent Systems

• Forward chaining
• A data-driven search in a rule-based system

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Reasoning in Intelligent Systems

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

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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 systems reasoning and justify its
  conclusions

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Explanation and Metaknowledge

• Why explanations
• How explanations
• Other explanations
• Who
• What
• Where
• When
• Why
• How

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Explanation and Metaknowledge

• Metaknowledge
• Static explanation
• In an ES, an association of fixed explanation
  text with a rule to explain the rules meaning.
• Dynamic explanation
• In ES, an explanation facility that reconstructs
  the reasons for its actions as it evaluates rules

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Explanation and Metaknowledge

• Categorization of the explanation methods
• Trace, or line of reasoning
• Justification
• Strategy

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Inferencing with Uncertainty
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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

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Inferencing with Uncertainty

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

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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
• DempsterShafer theory of evidence
• Belief function
• The representation of uncertainty without the
  need to specify exact probabilities

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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

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Inferencing with Uncertainty

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

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Expert Systems Development
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Expert Systems Development

• Phase I Project initialization
• Phase II System analysis and design
• Conceptual design
• Development strategy and methodology
• Sources of knowledge

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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

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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)

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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

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Expert Systems Development

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

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Expert Systems Development

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

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Knowledge Acquisition and the Internet

• The Internet as a communication medium
• The Internet as an open knowledge source