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Knowledge Acquisition and Engineering: Methods and Strategies for Real-Time Systems

Learn about knowledge engineering processes, acquisition approaches, inference strategies, and more. Explore the development of a real-time knowledge-based system at Eli Lilly through knowledge elicitation, fusion, and testing methods. Understand the role of knowledge engineers in acquiring, representing, validating, and maintaining knowledge across various sources. Discover techniques such as manual, semi-automatic, and automatic elicitation methods and how multiple experts contribute to the acquisition process.

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Knowledge Acquisition and Engineering: Methods and Strategies for Real-Time Systems

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  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

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

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

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

  9. 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

  10. 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

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

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

  13. 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

  14. 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

  15. 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

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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

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

  32. 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

  33. 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

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

  35. 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

  36. 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

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

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

  39. 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

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

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

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