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Expert systems


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Expert systems

  2. 2. Artificial intelligence Vision systems Learning systems Robotics Expert systems Neural networks Natural language processing
  3. 3. EXPERT SYSTEMS Expert systems are designed to solve real problems in a particular domain that normally would require a human expert. It can solve many types of problems Developing an expert system involves extracting relevant knowledge from human experts in the area of problem, called domain experts.
  4. 4. Components of Expert System         Knowledge acquisition facility Knowledge base Knowledge-based management system Inference engine, Work space Explanation facility Reasoning capability and , User interface.
  5. 5. Characteristics of ES Expert system is capable of handling challenging decision problems and delivering solutions. Expert system uses knowledge rather than data for solution. Much of the knowledge is heuristicbased rather than algorithmic. Expert system has the capability to explain how the decision was made.
  6. 6. Characteristics contd… Can… Explain their reasoning or suggested decisions Display intelligent behavior Draw conclusions from complex relationships Provide portable knowledge Expert system shell A collection of software packages and tools used to develop expert systems
  7. 7. Limitations of Expert Systems Not widely used or tested Limited to relatively narrow problems Cannot readily deal with “mixed” knowledge Possibility of error Cannot refine own knowledge base Difficult to maintain May have high development costs Raise legal and ethical concerns
  8. 8. Capabilities of Expert Systems Strategic goal setting Planning Design Decision making Quality control and monitoring Diagnosis Explore impact of strategic goals Impact of plans on resources Integrate general design principles and manufacturing limitations Provide advise on decisions Monitor quality and assist in finding solutions Look for causes and suggest solutions
  9. 9. Components of Expert System Fuzzy logic A specialty research area in computer science that allows shades of gray and does not require everything to be simply yes/no, or true/false Backward chaining A method of reasoning that starts with conclusions and works backward to the supporting facts Forward chaining A method of reasoning that starts with the facts and works forward to the conclusions
  10. 10. Explanation facility Inference engine Knowledge base Knowledge base acquisition facility User interface Experts User
  11. 11. Rules for a Credit Application Mortgage application for a loan for Rs.100,000 to Rs.200,000 If there are no previous credits problems, and If month net income is greater than 4x monthly loan payment, and If down payment is 15% of total value of property, and If net income of borrower is > Rs.25,000, and If employment is > 3 years at same company Then accept the applications Else check other credit rules
  12. 12. Explanation Facility A part of the expert system that allows a user or decision maker to understand how the expert system arrived at certain conclusions or results
  13. 13. Knowledge Acquisition Facility Knowledge acquisition facility  Provides a convenient and efficient means of capturing and storing all components of the knowledge base Knowledge base Knowledge acquisition facility Expert
  14. 14. Expert Systems Development Determining requirements Identifying experts Domain Construct expert system components • The area of knowledge addressed by the expert system. Implementing results Maintaining and reviewing system
  15. 15. Participants in Expert Systems Development and Use Domain expert The individual or group whose expertise and knowledge is captured for use in an expert system Knowledge user The individual or group who uses and benefits from the expert system Knowledge engineer Someone trained or experienced in the design, development, implementation, and maintenance of an expert system
  16. 16. Expert system Domain expert Knowledge engineer Knowledge user
  17. 17. Evolution of Expert Systems Software Expert system shell Collection of software packages & tools to design, develop, implement, and maintain expert systems Ease of use high low Traditional programming languages Before 1980 Special and 4th generation languages 1980s Expert system shells 1990s
  18. 18. Limitations of Expert Systems Not widely used or tested Limited to relatively narrow problems Cannot readily deal with “mixed” knowledge Possibility of error Cannot refine own knowledge base Difficult to maintain May have high development costs Raise legal and ethical concerns
  19. 19. When to Develop an ES?  The problem cannot be specified in terms of a well-defined algorithm. The problem requires consistency and standardization. The domain or problem area is narrow or limited.  When the task is hazardous. There is scarcity of experts in the area.  The problem involves complex logic or a large number of rules. Human experts have successfully solved similar problems
  20. 20. Advantages of ES It enhances decision quality. It reduces the cost of consulting experts for problem solving. It provides quick and efficient solutions to problems in narrow area of specialization. It offers high reliability of expert suggestions or decisions. It gathers scarce expertise and uses it efficiently.
  21. 21. Advantages of ES contd… It can tackle very complex problems that are difficult for human experts to solve. It can work on standard computer hardware. It can not only give solutions, but also the decision logic and how the solution was arrived at.
  22. 22. Limitations of ES The knowledge base may not be complete Each problem is different. Hence the solution from a human expert too may be different Expensive to build and maintain Takes long time to develop and fine tune ES Large ES is difficult to build and maintain
  23. 23. THANKS Share your ideas!