MIS 07 Expert Systems

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The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune.
Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik)
http://www.tusharkute.com

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MIS 07 Expert Systems

  1. 1. Management information system<br />Third Year Information Technology<br />Part 07<br />Expert Systems<br />Tushar B Kute,<br />Department of Information Technology,<br />Sandip Institute of Technology and Research Centre, Nashik<br />http://www.tusharkute.com<br />
  2. 2. Expert system architecture (1)<br />The typical architecture of an e.s. is often described as follows:<br />user<br />interface<br />user<br />inference<br />engine<br />knowledge<br />base<br />
  3. 3. Expert system architecture (1)<br />The inference engine and knowledge base are separated because:<br />the reasoning mechanism needs to be as stable as possible;<br />the knowledge base must be able to grow and change, as knowledge is added;<br />this arrangement enables the system to be built from, or converted to, a shell.<br />
  4. 4. Expert system architecture (2) <br />It is reasonable to produce a richer, more elaborate, description of the typical expert system.<br />A more elaborate description, which still includes the components that are to be found in almost any real-world system, would look like this:<br />
  5. 5. Expert system architecture (2)<br />
  6. 6. Expert system architecture (2)<br />
  7. 7. The system holds a collection of general principles which can potentially be applied to any problem - these are stored in the knowledge base.<br />The system also holds a collection of specific details that apply to the current problem (including details of how the current reasoning process is progressing) - these are held in working memory.<br />Both these sorts of information are processed by the inference engine.<br />Expert system architecture (2)<br />
  8. 8. Expert system architecture (2)<br />Any practical expert system needs an explanatory facility. It is essential that an expert system should be able to explain its reasoning.<br />
  9. 9. Expert & Knowledge-Based Systems<br />One of AI’s greatest areas of success was the development of large-scale problem solving systems<br />Originally called expert systems, they would mimic the problem solving processes of domain experts<br />Such as doctors performing diagnosis, or engineers performing design, or wall street analysts selecting stock transactions<br />Expert systems were originally developed by hand<br />And most commonly in some Lisp dialect<br />It was discovered that many problems were being solved by chaining through rules (if-then statements) that would operate on a collection of facts and partial conclusions<br />Called working memory<br />These rule-based systems led to the first AI tools or shells<br />Today, to simplify expert system creation, most people use these AI shells – you just fill in the knowledge, the problem solving processes are already implemented<br />
  10. 10. Introduction: Dendral<br />The Dendral system (DENDRiticALgorithm) was the first expert system, developed in the 1960s<br />The idea was, given mass spectrogram data, determine what the chemical composition was<br />The approach: plan-generate-and-test with human feedback<br />This is a constrained search technique<br />Generate a hypothesis: a possible chemical compound<br />Test the hypothesis: use a series of heuristics and subprograms to determine if the chemical compound generated is plausible given the data<br />If so, show it to the user – the user can steer the direction that DENDRAL takes next by suggesting what chemical elements should be added or removed from the generated hypothesis compound<br />DENDRAL repeats until an answer is found acceptable that does not violate the constraints as dictated by the data<br />
  11. 11. Pattern Matching<br />Expert Systems consisted of two major components<br />A knowledge base<br />The knowledge that the system would use to come to conclusions<br />In Dendral, this would be chemical elements, compounds, the mass spectrogram data that could arise from any element<br />In Mycin, this would be bacteriological infection knowledge<br />An inference engine<br />How to apply the knowledge<br />For Dendral, this was done by generate-and-test, a very weak form of search<br />Mycin would perform backward chaining through rules<br />More generically though, the inference engine would embody some form of pattern matching<br />One of the first systems that clearly demonstrated pattern matching was Eliza<br />Eliza was a spoof of an expert system but the code will show us how pattern matching works<br />
  12. 12. Eliza<br />Created by Joseph Weizenbaum<br />This system would act like a Rogerian psychoanalyst by holding a dialog with a person<br />the dialog would be consist of the doctor (Eliza) asking questions, the human responding, and the doctor using the response to ask another question<br />Weizenbaum wrote the program so that it would generate an English response/question based on a group of patterns<br />If the user sentence matched a pattern, this pattern would be used to generate the next sentence/question<br />Sample dialog:<br />H: Men are all alike<br />E: In what way?<br />H: They’re always bugging us about something or another.<br />E: Can you be more specific?<br />H: He says I’m depressed much of the time.<br />E: I am sorry to hear you are depressed.<br />H: It’s true. I am unhappy.<br />E: Do you think coming here will help you not to be unhappy?<br />H: I need some help, that much seems certain.<br />E: What would it mean to you if you got some help?<br />H: Perhaps I could learn to get along with my mother.<br />E: Tell me more about your family<br />
  13. 13. How Eliza Works<br />Repeat<br />Input a sentence<br />Find a rule in the Eliza knowledge-base that matches the pattern<br />Attempt to perform pattern match (see next slide)<br />Attempt to perform segment match (see two slides)<br />If rule found, select one of the responses randomly (each pattern will have at least one response)<br />Fill in any variables<br />Substitute values (you for I, I for you, me for you, am for are, etc)<br />Respond<br />Until user quits<br />Most of the work goes on in the pattern match and segment match functions<br />
  14. 14. Artificial intelligence systems<br />It falls into three basic categories:<br />Expert systems.<br />Natural Language Processing<br />Perception Systems.<br />AI is a software technique applied to the non-numeric data expressed in terms of symbols, statements and patterns.<br />It uses methods of symbolic processing, social and scientific reasoning and conceptual modeling for solving the problems.<br />
  15. 15. Categories of AI<br />Artificial Intelligence System<br />Natural Language<br />Expert<br />Perception<br />Uses<br />Uses<br />Uses<br />Native Language Knowledge<br />Knowledge<br />Size, Shape, Image, Voice<br />Applies<br />Applies<br />Applies<br />Language Reasoning <br />Human Like Reasoning <br />Sensing Abilities for Reasoning <br />
  16. 16. AI Applications<br />Uses Human Information Processing Capability<br />Uses Computer Intelligence for producing Human Like Capacity<br />Uses Human capabilities in speech recognition, Multi Sensory Interfacing<br />AI Applications<br />Robotics Applications<br />Natural Interface Applications<br />Intelligent Agents<br />Fuzzy Logic<br />Learning System<br />Expert System<br />Robot Systems for doing Human Jobs<br />VR Systems<br />
  17. 17. Knowledge based expert systems<br />Decision making or problem solving is a unique situation riddled with uncertainty and complexity, dominated by resource constraints and a possibility of several goals. In such cases, flexible systems (open systems) are required to solve the problems. <br />Most of such situations, termed as the unstructured situations, adopt two methods of problem solving, generalized or the knowledge based expert systems.<br />
  18. 18. KBES<br />To build a KBES, certain prerequisites are required. The first prerequisite is that a person with the ability to solve the problem with knowledge based reasoning should be available.<br />Second prerequisite is that, such an expert should be able to articulate the knowledge to the specific problem characteristics.<br />Knowledge in KBES is defined as a mix of theory of the subject, knowledge of its application, organized information and the data of problems and its solutions.<br />
  19. 19. reference<br /><ul><li>WamanJawadekar, "Management Information Systems” , 4th Edition, Tata McGraw-Hill Publishing Company Limited.
  20. 20. E. Turban, J. Aronson, T.P. Liang, R. Sharda, “Decision Support and Business Intelligence Systems”, 8th Edition, Pearson Education.</li>

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