1 / 46

Chapter 11 Knowledge Acquisition, Representation, and Reasoning

Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition. Chapter 11 Knowledge Acquisition, Representation, and Reasoning. Learning Objectives. Understand the nature of knowledge.

lorimer
Download Presentation

Chapter 11 Knowledge Acquisition, Representation, and Reasoning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

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

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

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

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

  7. Knowledge • Sources • Documented • Written, viewed, sensory, behavior • Undocumented • Memory • Acquired from • Human senses • Machines © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  8. Knowledge • Levels • Shallow • Surface level • Input-output • Deep • Problem solving • Difficult to collect, validate • Interactions betwixt system components © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  9. Knowledge • Categories • Declarative • Descriptive representation • Procedural • How things work under different circumstances • How to use declarative knowledge • Problem solving • Metaknowledge • Knowledge about knowledge © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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

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

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

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

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

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

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

  17. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  32. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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

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

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

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

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

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

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

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

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

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

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

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

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

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

More Related