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