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Chapter 18

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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  1. Chapter 18 KNOWLEDGE ACQUISITION, REPRESENTATION, AND REASONING

  2. 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)

  3. 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

  4. Concepts of Knowledge Engineering • The knowledge-engineering process • Knowledge acquisition • Knowledge representation • Knowledge validation • Inferencing • Explanation and justification

  5. 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

  6. Concepts of Knowledge Engineering

  7. 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

  8. 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

  9. 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

  10. 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

  11. The Scope and Types of Knowledge

  12. 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

  13. 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

  14. Methods of Acquiring Knowledge from Experts

  15. Methods of Acquiring Knowledge from Experts • Elicitation of knowledge The act of extracting knowledge, generally automatically, from nonhuman sources; machine learning

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. Methods of Acquiring Knowledge from Experts

  27. 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

  28. Methods of Acquiring Knowledge from Experts • Automatic knowledge modeling methods • Typical methods for knowledge discovery • Inductive learning • Neural computing • Genetic algorithms

  29. 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

  30. Acquiring Knowledge from Multiple Experts • Multiple-expert scenarios • Individual experts • Primary and secondary experts • Small groups • Panels

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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)

  39. 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

  40. 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

  41. 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

  42. 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

  43. Representation of Knowledge

  44. 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

  45. Representation of Knowledge

  46. 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

  47. Representation of Knowledge

  48. Representation of Knowledge

  49. Representation of Knowledge • Decision table A table used to represent knowledge and prepare it for analysis

  50. Representation of Knowledge

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