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Artificial Intelligence: Knowledge Engineering
The document discusses the requirements and components of expert systems. It notes that expert systems must have a narrow problem area requiring significant human expertise. They must perform at an expert level and be able to explain their reasoning. Structurally, they require a knowledge base containing domain expertise separated from inference procedures, as well as a working memory, inference engine, explanation facility, and user interface. The knowledge base stores facts and rules, the inference engine applies rules to derive new facts, and working memory stores known facts.
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Artificial Intelligence: Knowledge Engineering
- 1. Knowledge Engineering Domain Expert Knowledge Engineer Expert System • IncludeEnd User from beginning • Provides choices • Incremental Development
- 2. Requirements of ExpertSystems • Functional Requirements • Structural Requirements
- 3. Functional Requirements ofES • Problem Area – “…solve problems efficiently and effectively in a narrow problem area.” (Waterman, 1986, p.xvii) – “…typical, pertains to problems that can be symbolically represented.” (Liebowitz, 1988, p.3) • Not number crunching problems • Can be qualitative or quantitative variables
- 4. Functional Requirements ofES • Problem Difficulty – “…apply expert knowledge to difficult real world problems.” (Waterman, 1986, p.18) – “…solve problems that are difficult enough to require significant human expertise for their solution.” (Edward Feigenbaum in Harmon & King, 1985, p.5)
- 5. Functional Requirements ofES • Performance Requirement – “the ability to perform at the level of an expert…” (Liebowitz, 1988, p.3) – “…matches a competent level of human expertise in a particular field.” (Bishop, 1986, p.38)
- 6. Functional Requirements ofES • Explain Reasoning – “incorporation of explanation processes…” (Liebowitz, 1988, p.3) – The ability to explain how the system arrived at a solution • From Decision Support – People more confident w/conclusion because understand how system arrived to a conclusion – Also understand how people reason – Rarely used in Medicine: liability falls upon the System Developer
- 7. Structural Requirements ofES • Have to have a Knowledge Base • Knowledge Component – Encapsulation of human expertise – “A computer based system in which representations of expertise are stored…” (Edwards and Connell, 1989, p.3) – “The knowledge of an expert system consists of facts and heuristics. The ‘facts’ constitute a body of information that is widely shared, publicly available, and generally agreed upon by experts in the field.” (Edward Feigenbaum in Harmon & King, 1985, p.5)
- 8. Structural Requirements ofES • Separate knowledge and control – “…make domain knowledge explicit and separate from the rest of the system.” (Waterman, 1986, p.18) – Knowledge (need a knowledge base) and Meta- knowledge (how to use the knowledge) • Knowledge and Meta-knowledge are two different things
- 9. Structural Requirements ofES • Use inference procedures - heuristics (rule of thumb) - uncertainty – “…an intelligent computer program that uses knowledge and inference procedures.” (Edward Feigenbaum in Harmon & King, 1985, p.5) – “Exhibit intelligent behavior by skillful application of heuristics.” (Waterman, 1986, p.18)
- 10. Components of ES/KBS •Knowledge Base • Inference Engine • Working Memory • Explanation Facility • User Interface – Front End: the part the end user uses to retrieve information – Different from system to system – Usually “yes/no” questions
- 11. Knowledge Base • Collectionof domain knowledge, heuristics or rules of thumb – Put domain knowledge is a knowledge base • Representation Methods (may be 1000’s of rules) – Rules: if-then-else • Each rule has a condition part and an action part • Example – IF customer’s billing category is not set » AND customer has good payment history – THEN » Set the customer’s billing category to priority – Frames – Semantic Net
- 12. Inference Engine • Anexecutor of knowledge to solve a problem • Reasoning process (the inference process) • Two Parts – Interpreter • Decides how to apply the rules to infer new knowledge • Then passes to the scheduler – Scheduler • Decides the order in which the rules should be applied
- 13. Working Memory • Storesa collection of true facts • Serves as a global collection of known facts which an inference engine derives about a problem – When inference engine first used, contains no information – When first question answered, now has a fact within memory • Example - Animal Knowledge Base – Rule1: Animal has Hair? » Yes » Animal is a mammal – Rule 2: Animal gives milk? » Yes » Animal is a mammal • Reasoning Process uses Iteration
- 14. Expert System Rules Facts Interpreter/ Scheduler 1stPage:KnowledgeBaseWorkingMemory 2ndPage:InferenceEngine StoresFacts InferenceInterphase 1)Hashair? -yes:Interpreterpassesto Facts
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- 17. Explanation Facility • Thecomponent of an ES that explains how solutions were reached and justifies the steps used to reach them • Three Types – How • It explains how the system reached a particular state (backtrack) – What If • It explains what would have happened if a particular fact or rule had been different (“Does not give milk…” Sensitivity Analysis/Forecasting) – Why Not • It explains why an expected conclusion were not reached (Must have MORE knowledge)
- 18. User Interface • Userfriendly interface to the system – Example: Does animal have hair? Does animal chew meat? • A user answers and/or asks questions
- 19. Will ES workfor My Problem? • When is ES development possible? – Task does not require common sense • Impossible to code everything into system – Task requires only cognitive skills • Motor Skills will not work – Skills of Quarterback » Motor Skills » Cognitive Skills – Experts can articulate their methods • Knowing this, this, & this… I arrive at this. • Many years of experience – Genuine experts exist (task) – Experts agree on solutions (multiple domain experts must agree) – Task is not too difficult (task clearly understood) – Task is not poorly understood – Robotics & Systems not in this field
- 20. Will ES workfor My Problem? • Need to know your End Users in order to sell – Chief Information Officer (CIO) is interested in a business solution, not a technical solution • Technical solution is for Computer Science • When is ES development appropriate? – Task requires symbol manipulation (not a number crunching problem) – Task required heuristic solutions (rule of thumb decision) – Task is not too easy (don’t try Tick-Tack-Toe) – Task has practical value – Task is of manageable size
- 21. Will ES workfor My Problem? • Resistance from Management or Experts – People Resistant to change – Must champion your cause (persuasion) – Expert System brings change • Can change corporate culture • Resistance from End Users – Threat of losing job & usefulness
- 22. Will ES workfor MY Problem? • Still trying to determine what Intelligent Technology can do for NASA – Saving money – So what? • Have to put into User’s View – Selling the product • NASA – Cheaper – Faster – More reliable – Smaller • Then will be willing to listen to you about your Intelligent System
- 23. References • Bishop, Peter.Fifth Generation Computers Concepts, Implementations & Uses, 1986, Chichester, England: Ellis Horwood Ltd. • Edwards, Alex and Connel, N.A.D. Expert Systems in Accounting, 1989, Herfordshire, UK: Prentice Hall International (UK) Ltd. • Harmon, Paul and King, David. Expert Systems: Artificial Intelligence in Business. 1985, New York: Wiley. • Liebowitz, Jay, Introduction to Expert Systems, 1988, Santa Cruz, CA: Mitchell Publishing, Inc. • Waterman, Donald A. A Guide to Expert Systems, 1986, reading, MA: Addison-Wesley.