Knowledge Acquisition, Representation, and Reasoning

1. Knowledge Acquisition, Representation, and Reasoning By Dr.S.Sridhar,Ph.D.,RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc.email : drssridhar@yahoo.comweb-site : http://drsridhar.tripod.com

2. Learning Objectives • Understand the nature of knowledge. • Learn the knowledge engineering processes. • Evaluate different approaches for knowledge acquisition. • Examine the pros and cons of different approaches. • Illustrate methods for knowledge verification and validation. • Examine inference strategies. • Understand certainty and uncertainty processing.

3. Development of a Real-Time Knowledge-Based System at Eli Lilly Vignette • Problems with fermentation process • Quality parameters difficult to control • Many different employees doing same task • High turnover • Expert system used to capture knowledge • Expertise available 24 hours a day • Knowledge engineers developed system by: • Knowledge elicitation • Interviewing experts and creating knowledge bases • Knowledge fusion • Fusing individual knowledge bases • Coding knowledge base • Testing and evaluation of system

4. Knowledge Engineering • Process of acquiring knowledge from experts and building knowledge base • Narrow perspective • Knowledge acquisition, representation, validation, inference, maintenance • Broad perspective • Process of developing and maintaining intelligent system

5. Knowledge Engineering Process • Acquisition of knowledge • General knowledge or metaknowledge • From experts, books, documents, sensors, files • Knowledge representation • Organized knowledge • Knowledge validation and verification • Inferences • Software designed to pass statistical sample data to generalizations • Explanation and justification capabilities

7. Knowledge • Sources • Documented • Written, viewed, sensory, behavior • Undocumented • Memory • Acquired from • Human senses • Machines

8. Knowledge • Levels • Shallow • Surface level • Input-output • Deep • Problem solving • Difficult to collect, validate • Interactions betwixt system components

9. Knowledge • Categories • Declarative • Descriptive representation • Procedural • How things work under different circumstances • How to use declarative knowledge • Problem solving • Metaknowledge • Knowledge about knowledge

10. Knowledge Engineers • Professionals who elicit knowledge from experts • Empathetic, patient • Broad range of understanding, capabilities • Integrate knowledge from various sources • Creates and edits code • Operates tools • Build knowledge base • Validates information • Trains users

12. Elicitation Methods • Manual • Based on interview • Track reasoning process • Observation • Semiautomatic • Build base with minimal help from knowledge engineer • Allows execution of routine tasks with minimal expert input • Automatic • Minimal input from both expert and knowledge engineer

13. Manual Methods • Interviews • Structured • Goal-oriented • Walk through • Unstructured • Complex domains • Data unrelated and difficult to integrate • Semistructured

14. Manual Methods • Process tracking • Track reasoning processes • Protocol analysis • Document expert’s decision-making • Think aloud process • Observation • Motor movements • Eye movements

15. Manual Methods • Case analysis • Critical incident • User discussions • Expert commentary • Graphs and conceptual models • Brainstorming • Prototyping • Multidimensional scaling for distance matrix • Clustering of elements • Iterative performance review

16. Semiautomatic Methods • Repertory grid analysis • Personal construct theory • Organized, perceptual model of expert’s knowledge • Expert identifies domain objects and their attributes • Expert determines characteristics and opposites for each attribute • Expert distinguishes between objects, creating a grid • Expert transfer system • Computer program that elicits information from experts • Rapid prototyping • Used to determine sufficiency of available knowledge

18. Semiautomatic Methods, continued • Computer based tools features: • Ability to add knowledge to base • Ability to assess, refine knowledge • Visual modeling for construction of domain • Creation of decision trees and rules • Ability to analyze information flows • Integration tools

19. Automatic Methods • Data mining by computers • Inductive learning from existing recognized cases • Neural computing mimicking human brain • Genetic algorithms using natural selection

20. Multiple Experts • Scenarios • Experts contribute individually • Primary expert’s information reviewed by secondary experts • Small group decision • Panels for verification and validation • Approaches • Consensus methods • Analytic approaches • Automation of process through software usage • Decomposition

21. Automated Knowledge Acquisition • Induction • Activities • Training set with known outcomes • Creates rules for examples • Assesses new cases • Advantages • Limited application • Builder can be expert • Saves time, money

22. Automated Knowledge Acquisition • Difficulties • Rules may be difficult to understand • Experts needed to select attributes • Algorithm-based search process produces fewer questions • Rule-based classification problems • Allows few attributes • Many examples needed • Examples must be cleansed • Limited to certainties • Examples may be insufficient

23. Automated Knowledge Acquisition • Interactive induction • Incrementally induced knowledge • General models • Object Network • Based on interaction with expert • interviews • Computer supported • Induction tables • IF-THEN-ELSE rules

24. Evaluation, Validation, Verification • Dynamic activities • Evaluation • Assess system’s overall value • Validation • Compares system’s performance to expert’s • Concordance and differences • Verification • Building and implementing system correctly • Can be automated

26. Production Rules • IF-THEN • Independent part, combined with other pieces, to produce better result • Model of human behavior • Examples • IF condition, THEN conclusion • Conclusion, IF condition • If condition, THEN conclusion1 (OR) ELSE conclusion2

27. Artificial Intelligence Rules • Types • Knowledge rules • Declares facts and relationships • Stored in knowledge base • Inference • Given facts, advises how to proceed • Part of inference engines • Metarules

28. Artificial Intelligence Rules • Advantages • Easy to understand, modify, maintain • Explanations are easy to get. • Rules are independent. • Modification and maintenance are relatively easy. • Uncertainty is easily combined with rules. • Limitations • Huge numbers may be required • Designers may force knowledge into rule-based entities • Systems may have search limitations; difficulties in evaluation

29. Semantic Networks • Graphical depictions • Nodes and links • Hierarchical relationships between concepts • Reflects inheritance

30. Frames • All knowledge about object • Hierarchical structure allows for inheritance • Allows for diagnosis of knowledge independence • Object-oriented programming • Knowledge organized by characteristics and attributes • Slots • Subslots/facets • Parents are general attributes • Instantiated to children • Often combined with production rules

31. Knowledge Relationship Representations • Decision tables • Spreadsheet format • All possible attributes compared to conclusions • Decision trees • Nodes and links • Knowledge diagramming • Computational logic • Propositional • True/false statement • Predicate logic • Variable functions applied to components of statements

33. Reasoning Programs • Inference Engine • Algorithms • Directs search of knowledge base • Forward chaining • Data driven • Start with information, draw conclusions • Backward chaining • Goal driven • Start with expectations, seek supporting evidence • Inference/goal tree • Schematic view of inference process • AND/OR/NOT nodes • Answers why and how • Rule interpreter

34. Explanation Facility • Justifier • Makes system more understandable • Exposes shortcomings • Explains situations that the user did not anticipate • Satisfies user’s psychological and social needs • Clarifies underlying assumptions • Conducts sensitivity analysis • Types • Why • How • Journalism based • Who, what, where, when, why, how • Why not

35. Generating Explanations • Static explanation • Preinsertion of text • Dynamic explanation • Reconstruction by rule evaluation • Tracing records or line of reasoning • Justification based on empirical associations • Strategic use of metaknowledge

36. Uncertainty • Widespread • Important component • Representation • Numeric scale • 1 to 100 • Graphical presentation • Bars, pie charts • Symbolic scales • Very likely to very unlikely

37. Uncertainty • Probability Ratio • Degree of confidence in conclusion • Chance of occurrence of event • Bayes Theory • Subjective probability for propositions • Imprecise • Combines values • Dempster-Shafer • Belief functions • Creates boundaries for assignments of probabilities • Assumes statistical independence

38. Certainty • Certainty factors • Belief in event based on evidence • Belief and disbelief independent and not combinable • Certainty factors may be combined into one rule • Rules may be combined

39. Expert System Development • Phases • Project initialization • Systems analysis and design • Prototyping • System development • Implementation • Postimplementation

40. Project Initialization • Identify problems • Determine functional requirements • Evaluate solutions • Verify and justify requirements • Conduct feasibility study and cost-benefit analysis • Determine management issues • Select team • Project approval

41. Systems Analysis and Design • Create conceptual system design • Determine development strategy • In house, outsource, mixed • Determine knowledge sources • Obtain cooperation of experts • Select development environment • Expert system shells • Programming languages • Hybrids with tools • General or domain specific shells • Domain specific tools

42. Prototyping • Rapid production • Demonstration prototype • Small system or part of system • Iterative • Each iteration tested by users • Additional rules applied to later iterations

43. System Development • Development strategies formalized • Knowledge base developed • Interfaces created • System evaluated and improved

44. Implementation • Adoption strategies formulated • System installed • All parts of system must be fully documented and security mechanisms employed • Field testing if it stands alone; otherwise, must be integrated • User approval

45. Postimplementation • Operation of system • Maintenance plans • Review, revision of rules • Data integrity checks • Linking to databases • Upgrading and expansion • Periodic evaluation and testing

46. Internet • Facilitates knowledge acquisition and distribution • Problems with use of informal knowledge • Open knowledge source