1. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-1 Chapter 11 Knowledge Acquisition, Representation, and Reasoning Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition

2. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-2 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. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-3 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. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-4 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. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-5 11.2 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 make inferences based on knowledge to non expert. Explanation and justification capabilities

6. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-6

7. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-7 Knowledge Sources –Documented Written, viewed, sensory, behavior –Undocumented Memory –Acquired from Human senses Machines ( dbases, Via Internet)

8. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-8 Levels of Knowledge –Shallow: IF gasoline tank is empty, THEN car will not start. Surface level to deal with very specific situation Input-output If – Then rules Insufficient in describing complex situation –Deep : Investigate deeper the relation between lack of gasoline and car won’t start. Human Problem solving Difficult to collect, validate Interactions between system components

9. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-9 Categories of Knowledge –Declarative Descriptive representation that tells facts –Procedural How things work under different circumstances How to use declarative knowledge –Problem solving step-by-step –Metaknowledge Knowledge about knowledge. About the operation of Knowledge-based sys. About their reasoning capabilities.

10. 11.4 Methods of Knowledge acquisition from experts The roles of important participants: 1.Knowledge Eng. Knowledge structuring, tool designer, catalyst between expert and end-user. 1.The Expert. 1.The End-User © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-10

11. Difficulties in Know. Eng. Acquiring knowledge from expert is not an easy task. Experts in ability to articulate their knowledge. Experts lack of time or unwilling to cooperate. Testing and refining know. Is complicated. Poor definition of knowledge elicitation. Collecting know. From one source. Problematic interpersonal comm. Factors. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-11

12. Know. Eng. Skills Know. Eng. are human professionals who are able to comm. with experts & consolidate knowledge fro various sources to build a valid Knowledge-Base. Computer skills. Tolerance and ambivalence (Unsure) Effective Comm. abilities (Sensitive, Tactful, Diplomatic) Broad educational background. Advanced socially sophisticated social skills. Fast-Learning capabilities. Wide experience in Knowledge Engineering. Empathy, patience. Logical thinking., Persistence Versatility and inventiveness Self-Confidence. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-12

13. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-13 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

14. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-14

15. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-15 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

16. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-16 Manual Methods Interviews –Structured Goal-oriented Walk through –Unstructured Complex domains Data unrelated and difficult to integrate –Semistructured

17. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-17 Manual Methods Process tracking –Track reasoning processes Protocol analysis –Document expert’s decision-making –Think aloud process Observation –Motor movements –Eye movements

18. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-18 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

19. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-19 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

20. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-20

21. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-21 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

22. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-22 Automatic Methods Data mining by computers Inductive learning from existing recognized cases Neural computing mimicking human brain Genetic algorithms using natural selection

23. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-23 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

24. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-24 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

25. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-25 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

26. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-26 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

27. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-27 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

28. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-28

29. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-29 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

30. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-30 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

31. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-31 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

32. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-32 Semantic Networks Graphical depictions Nodes and links Hierarchical relationships between concepts Reflects inheritance

33. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-33 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

34. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-34 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

35. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-35

36. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-36 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

37. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-37 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

38. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-38 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

39. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-39 Uncertainty Widespread Important component Representation –Numeric scale 1 to 100 –Graphical presentation Bars, pie charts –Symbolic scales Very likely to very unlikely

40. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-40 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

41. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-41 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

42. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-42 Expert System Development Phases –Project initialization –Systems analysis and design –Prototyping –System development –Implementation –Postimplementation

43. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-43 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

44. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-44 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

45. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-45 Prototyping Rapid production Demonstration prototype –Small system or part of system –Iterative –Each iteration tested by users –Additional rules applied to later iterations

46. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-46 System Development Development strategies formalized Knowledge base developed Interfaces created System evaluated and improved

47. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-47 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

48. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-48 Postimplementation Operation of system Maintenance plans –Review, revision of rules –Data integrity checks –Linking to databases Upgrading and expansion Periodic evaluation and testing

49. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-49 Internet Facilitates knowledge acquisition and distribution Problems with use of informal knowledge Open knowledge source