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Expert Systems

Expert Systems. Outline: Various Objectives in Creating Expert Systems Integration of AI Techniques into Applications MYCIN, AM Shells for Rule-based Expert Systems Engineering of Expert Systems. Reasons for Creating Expert Systems. Real-world applications serve various roles:

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Expert Systems

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  1. Expert Systems Outline: Various Objectives in Creating Expert Systems Integration of AI Techniques into Applications MYCIN, AM Shells for Rule-based Expert Systems Engineering of Expert Systems CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  2. Reasons for Creating Expert Systems Real-world applications serve various roles: 1. Automating services formerly performed by human experts. 2. Boosting the productivity of experts by creating initial solutions that must later be refined. 3. Providing services that would have been impractical to offer without AI (e.g., intelligent web search). 4. Capturing and immortalizing corporate knowledge in order to preserve it as a permanent asset. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  3. Integration of AI Techniques into Applications Individual AI techniques usually do not solve the full set of problems faced by an expert. Different AI techniques are needed for different parts of the system. Typical components of an expert system are: Knowledge acquisition, knowledge representation, inference, language understanding. Designing each of these so that they all work together can be viewed as an engineering problem. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  4. MYCIN Developed by Edward Shortliffe in 1976 at Stanford University in the artificial intelligence in medicine group. Carried on a text-based dialog with a physician. Contained knowledge about infectious diseases (bacterial infections), represented in IF-THEN rules with certainty values. Did not explicitly diagnose diseases but prescribed combinations of medications to cover for the likely causes. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  5. AM Developed by Douglas Lenat at Stanford in 1977. Simulated the activities of a mathematician, exploring number theory, coming up with conjectures and testing them empirically. Contained knowledge encoded as production rules. Came up with an explicit representation for the concept of prime numbers. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  6. Shells for Expert Systems In order to facilitate developing more expert systems, the domain-independent parts of MYCIN were made into a separate system: EMYCIN. Emptied of its knowledge, Empty MYCIN was ready to receive other knowledge. It was the first expert system “shell.” It provided a rule representation, inference engine capable of working with certainty values as well as logical statements, and a simple interface. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  7. Details of an Expert System Shell Rule language: 2 types of rules: IF-THEN rules and query rules. Inference engine: Forward chaining Backward chaining Interface: Poses questions and interprets answers. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  8. Sample Rules dot-pointillism-rule IF (texture (? painting) dots (? cf)) THEN (style (? painting) pointillism) WITH-CERTAINTY (* (? cf) 0.8) abdom-pain? IF (health-problem (? cf)) THEN (“Do you have abdominal pain?” (abdominal-pain) (knowledge-of-abd-pain)) WITH-CERTAINTY 1.0 normal rule query rule CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  9. Chaining Forward Chaining: Starting with the premises or given data, apply rules to derive more and more consequences. Backward Chaining: Starting with the goal, determine what subgoals must be achieved in order to attain the goal, and recursively attack the subgoals, until the subgoals are just premises or obvious conditions. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  10. Engineering of Expert Systems The 1980’s model: Create a team consisting of a knowledge engineer (KE) and a domain expert (DE);. The KE works with the DE to build a knowledge base consisting of rules. The rules are debugged by continual testing and refinement. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  11. Pedagogical Agents CAI: Computer-Assisted Instruction (automated presentation and drill) ITS: Intelligent Tutoring Systems (use natural language dialog and reasoning to enhance the experience) PA: Pedagogical Agents (enhances the tutor with additional agent-like qualities, such as an onscreen presence -- e.g., a talking head) CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  12. Pedagogical Agent Architecture Domain Knowledge Student Model Executive Pedagogical Knowledge Interface CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  13. Pedagogical Agent Components Domain Knowledge: Knowledge about the subject to be taught and learned. Pedagogical Knowledge: Knowledge about teaching strategies, optimal order of presentation, how to diagnose and fix misconceptions, etc. Student Model: A representation of the student’s beliefs, knowledge, motivations, learning style, and possibly past experiences. Interface: A means of communicating with the student, possibly including natural language understanding and generation, graphical display and sketch or gesture recognition, etc. Executive: An engine that follows a pedagogical plan, invoking the resources of the other components. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

  14. Expert Systems: Summary Expert Systems perform services and/or boost productivity. Expert Systems encapsulate and “immortalize” knowledge. Expert Systems are delivery modules for AI techniques. Expert Systems may be “brittle” if they lack common sense (as they all-too-often do). Expert Systems are often constructed using special “knowledge-engineering” tools such as: ES shells and expertise transfer programs. CSE 415 -- (c) S. Tanimoto, 2004 Expert Systems

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