11 expert systems___applied


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11 expert systems___applied

  1. 1. 11- 1 Chapter 11: Expert Systems and Applied Chapter 11: ArtificialSystem and Applied Artificial Intelligence Expert Intelligence First Edition Foundations of Information Systems Vladimir Zwass With Annotations By Dr. Betty Anne Jacobywin/McGraw-Hill © The McGraw-Hill Companies, Inc.., 1998
  2. 2. 11- 2Chapter Objectives 1. Define the field of artificial intelligence (AI) 2. Define an expert system (ES). 3. Specify and discuss the areas of ES application. 4. Specify the components of an expert system. 5. Define knowledge base and knowledge representation. 6. Explain what rule-based expert systems are. 7. Define fuzzy logic. 8. Specify the categories of expert system technology.
  3. 3. 11- 3Chapter Objectives, cont.9. Define the roles in expert system development.10. Specify the principal benefits and limitations of expert systems.11. Name other applied fields of artificial intelligence and discuss their potential role in information systems.12. Define neural networks and their capabilities.
  4. 4. 11- 4Defining Artificial Intelligence • AI deals with methods of developing systems that display aspects of intelligent behavior • AI systems imitate human capabilities of thinking and sensing
  5. 5. 11- 5Defining Artificial Intelligence: AI Systems 1. Symbolic Processing – Computers process symbols – AI applications process strings of characters that represent the real world – Symbols are arranged as lists, hierarchies, or networks and their interrelations 2. Nonalgorithmic Processing – Specified step by step procedures
  6. 6. 11- 6Defining Artificial Intelligence • Science and Technology • Computer Science • Biology • Psychology • Linguistics • Mathematics • Engineering • Goal: Develop computers that think (reasoning, learning, and problem solving), sense (see, hear, talk, feel), and walk
  7. 7. 11- 7Defining Artificial Intelligence:History and Evolution of AI • 1950- Turing Test- General problem solving test • 1960- AI as a field- Knowledge based expert systems • 1970- AI commercialization- Transaction processing and decision support systems • 1980- Artificial neural networks- Resembling the human brain • 1990- Intelligent Agents- Software that performs assigned tasks
  8. 8. 11- 8Capabilities of Expert Systems: General View • Expert System (ES) – Knowledge based system – Uses inferencing or reasoning procedure to solve problems that require human expertise • Knowledge Base – Domain of knowledge of the expert system • Heuristic Knowledge – Rules used by humans
  9. 9. 11- 9Applications of Expert Systems:Generic Categories of Expert System Applications • Classification – Identify an object • Diagnosis Systems – Infer malfunction from observable data • Monitoring – Continually observe behavior • Process Control – Control a physical process based on monitoring • Design – Configure a system according to specifications • Scheduling and Planning – Plan of action • Generation of Options – Alternative solutions to problems
  10. 10. 11- 10How Expert Systems Work • Knowledge Base – Organized collection of facts and heuristics about the systems domain • Knowledge Representation – Method to organize the knowledge base
  11. 11. 11- 11Structure of an Expert System Consultation Environment Development Environment (Use) (Knowledge Acquisition) User Expert Facts of Recommendation, the Case Explanation User Interface Explanation Knowledge Facility Engineer Inference Engine Facts of the Knowledge Case Acquisition Facility Working Memory Knowledge Domain Knowledge Base (Elements of Knowledge Base)
  12. 12. 11- 12How Expert Systems Work:Knowledge Representation • Frame Based Systems – Build powerful expert systems – The frame specifies the attributes of a complex object and its relationships • Production Rules – Rule Based expert systems – Knowledge is represented by production rules – Most common method of knowledge representation – IF part (Condition or Premise) and THEN part (Action or Conclusions_ – Explanation facility » How the system arrived at the recommendation » Uses natural language or numbers
  13. 13. 11- 13How Expert Systems Work:Inference Engine • Combines the facts of a specific case with the knowledge in the knowledge base to decide upon a recommendation – Reasoning in Rule Based systems » In rule based expert system, the inference engine controls the order in which the production rules are applied or “fired” and resolves conflicts for more than one applicable rule • Directs the user interface to query the user for further information – The facts are entered into working memory – Rules are applied by the inference engine until a goal state is produced or confirmed
  14. 14. 11- 14How Expert Systems Work:Inference Engine: Strategies • Forward Chaining – A data driven strategy – Inference from the facts of a case to a conclusion – Match the IF part with the facts available – Used to solve open ended problems of a design or planning • Backward Chaining – The inference engine matches the assumed hypothesis or conclusion which is the goal state with the conclusion or THEN part – If the hypothesis is not supported, then the system will attempt to prove another goal state – Used for limited in number and well defined problems – Use classification or diagnosis systems
  15. 15. 11- 15Inferencing Strategies Conclusion (Goals) Input Data Few Items Many Possibilities (For Example, User (For Example, a Specifications for Computer a Computer Configuration) System) (a) Forward Chaining: IF - Part Matches Shown
  16. 16. 11- 16Inferencing Strategies (Cont.) Input Data Conclusion (Goals) Extensive; Much of the Data Few Possibilities Obtained by the (Known in Advance System Querying ((For Example, the User (For Investment Options) Example, Investor’s Profile) (b) Backward Chaining: THEN - Part Matches Shown
  17. 17. 11- 17How Expert Systems Work:Uncertainty and Fuzzy Logic • Resembles human reasoning • Allows approximate values or inferences and incomplete or ambiguous data • Handles uncertainty • More flexible • Creative • Can be used to control manufacturing processes
  18. 18. 11- 18Expert System Technology • The tool selected for the project must match the capability of the projected expert system • Must be able to integrate with other subsystems and databases • The tool must match the qualifications of the project team
  19. 19. 11- 19Expert System Technology • Specific Expert Systems – Provide recommendations for a specific task domain • Expert System Shells – Shell without a knowledge base – Furnishes the ES developer with the inference engine, user interface, and the explanation and knowledge acquisition facilities – Domain specific shells » Incomplete specific expert systems • Expert System Development Environments – Run on engineering workstations, minicomputers, or mainframes – Integration with databases • High Level Programming Languages – LISP, C, C++
  20. 20. 11- 20Expert Systems Technologies Greater Complexity of Greater Higher-Level Problem and Flexibility Programming Environment Language Expert System Development Environment Generic Shell Domain-Specific Shell Greater Specific Expert Ease of Use System
  21. 21. 11- 21Roles in Expert System Development • Expert – Knowledge • Knowledge Engineer – Knowledge acquisition tactics include interviews, protocol analysis, observation, and analysis of cases – Must select a tool with the application of the knowledge acquisition facility • User – End user with a simple shell – Prototypes are used
  22. 22. 11- 22Development and Maintenance of Expert Systems 1. Problem Identification and Feasibility Analysis – The problem must be suitable for an expert to solve it. – Find an expert for the project – Cost effectiveness must be established 2. System Design and Expert System Technology Identification – The system is designed with integration other subsytems and databases – Domain knowledge – Knowledge and inferencing is established with simple cases 3. Development of Prototype – Knowledge Engineer works with the expert – Specific Tool is chosen for the project
  23. 23. 11- 23Development and Maintenance of Expert Systems(Continued) 4. Testing and Refinement of Prototype – Test with simple cases – Deficiencies in performance are noted. – End users test the prototypes. 5. Complete and Field the Expert System – The interaction with the environment,, users, and other information systems is tested – Documented – User training 6. Maintain the System – The system is kept current by updating the knowledge base – Interfaces with other information systems are maintained
  24. 24. 11- 24Development & Maintenance of ESs Problem Identification and Feasibility Analysis System Design and ES Technology Identification Development of Prototype Testing and Refinement of Prototype Yes Is the Performance Satisfactory? Complete and No Field the ES ES Ready for Use Maintain ES
  25. 25. 11- 25Expert Systems in Organizations: Benefits 1. An ES can complete its task faster than a human 2. Low error rate, and lower than human error rate 3. ESs make consistent recommendations. 4. ESs are a convenient vehicle for difficult sources of knowledge 5. ESs bring forth expertise 6. ESs can build organizational knowledge, as opposed to the knowledge of individuals 7. ESs can be used for training with a faster learning curve 8. The company can operate an ES in environments that are hazardous to humans
  26. 26. 11- 26Expert Systems in Organizations: Limitations 1. Limitations of the technology 2. Problems with knowledge acquisition 3. Operational domains as the principal area 4. Maintaining human expertise
  27. 27. 11- 27Overview of Applied Artificial Intelligence 1. Natural Language processing 2. Robotics 3. Computer Vision 4. Computerized Speech Recognition 5. Machine Learning
  28. 28. 11- 28Overview of Applied Artificial Intelligence • Natural Language Processing – Talk to computers and have them “understand” • Robotics – Artificial Intelligence, Engineering, and Physiology – Human like applications • Computer Vision – Simulation of the human senses – Visual scene recognition • Speech recognition – Understand speech, also of an unknown speaker
  29. 29. 11- 29Overview of Applied Artificial Intelligence • Machine Learning 1. Problem Solving Learning – Accumulate experience about rules 2. Case Based Learning – collecting cases from a knowledge base 3. Inductive Learning – Learning from examples – generate knowledge using rules
  30. 30. 11- 30Applied Fields of AI AI Com- Natural puterized Expert Computer Machine Language Robotics SpeechSystems Vision Learning Processing Recog- nition
  31. 31. 11- 31Neural Networks • Computing systems modeled on the human brain’s interconnected processing elements or neurons – 100 billion neuron brain cells • An array of interconnected processing elements accept inputs, processing, and then producing an output imitating the human brain • Requires sophisticated pattern recognition • Does not explain the conclusions they make • Can be made to recognize patterns, and then apply to new cases
  32. 32. 11- 32Key Terms in Chapter 11Artificial Intelligence (AI) Expert System DevelopmentExpert System (ES) EnvironmentKnowledge Base Knowledge EngineerHeuristic Knowledge Knowledge Acquisition FacilityKnowledge Engineering Natural Language ProcessingKnowledge Representation RobotRule-Based Expert System Computer VisionIF-THEN Rule Computerized SpeechExplanation Facility RecognitionInference Engine Machine LearningWorking Memory Neural NetworkForward ChainingBackward ChainingFuzzy LogicExpert System ShellDomain-Specific Shell