Expert systems from rk

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Expert systems from rk

  1. 1. Expert systems M.S.Rama krishna (0458-703 )
  2. 2. Definition <ul><li>What are Expert systems ? </li></ul><ul><ul><li>Knowledge based systems , & , Part of the Artificial Intelligence field. </li></ul></ul><ul><ul><li>The idea is to inject expert knowledge in to a computer system. </li></ul></ul><ul><ul><li>The primary purpose is to automate DECISION MAKING </li></ul></ul><ul><ul><li>Computer programs (if and then rules) that contain some subject-specific knowledge of one or more human experts </li></ul></ul><ul><ul><li>Made up of a set of rules that analyze user supplied information about a specific class of problems. </li></ul></ul><ul><ul><li>Systems that utilize reasoning capabilities and draw conclusions </li></ul></ul><ul><ul><li>COMMON PROGRAMING LANGUAGE SUPPORT </li></ul></ul><ul><ul><li>The most commonly used are </li></ul></ul><ul><ul><li>1) LISP. </li></ul></ul><ul><ul><li>2) Prolog. </li></ul></ul>
  3. 3. Components of an Expert System (1) <ul><li>Knowledge base : </li></ul><ul><ul><li>IT is the Nucleus of the expert system structure </li></ul></ul><ul><ul><li>Stores all relevant information, data, rules, cases, and relationships used by the expert system </li></ul></ul><ul><ul><li>Knowledge engineers : who translate the knowledge of real human experts into rules and strategies, create it. </li></ul></ul><ul><ul><li>These rules and strategies can change depending on the prevailing problem scenario </li></ul></ul>
  4. 4. Components of an Expert System(2) <ul><li>Inference Engine : </li></ul><ul><ul><li>Seeks information and relationships from the knowledge base and provides answers, predictions, and suggestions in the way a human expert would do </li></ul></ul><ul><ul><li>Match the premise patterns of the rules against elements in the working memory . </li></ul></ul><ul><ul><li>Explanation facility : </li></ul></ul><ul><ul><li>part of the expert system that allows a user or decision maker to understand how the expert system arrived at certain conclusions or results </li></ul></ul><ul><li>Set of Rules : </li></ul><ul><li>A conditional statement that links given conditions to actions or outcomes with if and then rules </li></ul>
  5. 5. INFERENCE ENGINE EXPERT KNOWLEDGE ARCHITECTURE OF SIMPLE EXPERT SYSTEM EXPERT KNOWLEDGE USER KNOWLEDGE BASE KNOWLEDEGE BASE ACQUITION FACILITY EXPLANATION FACILITY USER INTERFACE
  6. 6. Expert systems /Rule Based systems <ul><li>Rule-based or Expert systems - Knowledge bases consisting of hundreds or thousands of rules of the form: </li></ul><ul><ul><li>IF (condition) THEN (action). </li></ul></ul><ul><ul><li>Use rules to store knowledge (“rule-based”). </li></ul></ul><ul><ul><li>The rules are usually gathered from experts in the field being represented (“expert system”). </li></ul></ul><ul><li>IF ‘ x is A’ </li></ul><ul><li>THEN ‘ y is B’ </li></ul>antecedent or premise, consequent(or) conclusion. uent
  7. 7. Main Participants in Expert Systems <ul><li>Domain expert </li></ul><ul><ul><li>Who makes the system as expert by gathering or taken all the information from the expert , group , etc.. According to the different problem’s (or) scenario’s. </li></ul></ul><ul><li>Knowledge engineer </li></ul><ul><ul><li>Who makes the system as expert by injecting all the gathered information from the domain expert by implementing , developing , designed and maintained </li></ul></ul><ul><li>Knowledge user </li></ul><ul><ul><li>The individual or group who uses and benefits from the expert system </li></ul></ul>
  8. 8. Domain expert Knowledge engineer Knowledge user EXPERT SYSTEM
  9. 9. Knowledge Acquisition Facility <ul><ul><li>Knowledge acquisition facility </li></ul></ul><ul><ul><ul><li>Provides a convenient and efficient means of capturing and storing all components of the knowledge base </li></ul></ul></ul>Joe Expert KNOWLEDGE BASE KNOWLEDGE ACQUITION FACILITY
  10. 10. Methods used in the Expert system <ul><li>The basic types are: </li></ul><ul><li>1)Decision trees : </li></ul><ul><li>A tree is formed with the series of questions , responses where the answer is taken from the responses generated according to the scenario as end point. </li></ul><ul><li>2)Forward chaining : (data-driven) </li></ul><ul><li>A method of reasoning that starts with the facts and works forward to the conclusions </li></ul><ul><li>3)Back ward chaining : ( goal-driven ) </li></ul><ul><li>A method of reasoning that starts with conclusions and works backward to the supporting facts </li></ul><ul><li>4)State machines </li></ul><ul><li>5)Bayesian networks </li></ul><ul><li>6)Black board systems </li></ul><ul><li>7)Case based reasoning </li></ul>
  11. 11. <ul><li>Example : </li></ul><ul><li>Suppose we have three rules: </li></ul><ul><li>R1: If A and B then D </li></ul><ul><li>R2: If B then C </li></ul><ul><li>R3: If C and D then E </li></ul><ul><li>Forward chaining system : 1) facts are processed first, </li></ul><ul><li>2) keep using the rules to draw new conclusions given those facts </li></ul>
  12. 12. <ul><li>Rule 1 : </li></ul><ul><li>Rule 2: </li></ul><ul><li>Backward chaining : </li></ul>A B B E D C Rule 3 E D C B B A
  13. 13. <ul><li>State machine : </li></ul><ul><li>object :: number of different ‘states’, </li></ul><ul><li>How the object behaves, </li></ul><ul><li>switches between the states, </li></ul><ul><li>depends up on the current state it’s in state </li></ul><ul><li>Case based reasoning : </li></ul><ul><li>cases or instances of the problem, with solution that was found in a result that took place. </li></ul><ul><li>Rather than creating a set of rules, you just write an inference Engine </li></ul>
  14. 14. <ul><li>Bayesian networks: (probability based) </li></ul><ul><li>Gives a best-fit answer with probabilities </li></ul><ul><li>Consists of a table of inputs and outputs with the probability </li></ul><ul><li>particular input :: corresponding output </li></ul><ul><li>Black board systems: concept of neural network </li></ul>
  15. 15. Expert Systems Development Determining requirements Identifying experts Construct expert system components Implementing results Maintaining and reviewing system <ul><li>Domain </li></ul><ul><li>The area of knowledge addressed by the expert system. </li></ul>
  16. 16. Loan application <ul><li>Loan application for a loan for $100,000 to $200,000 </li></ul><ul><li>If Month net income is greater than 4x monthly loan payment, and </li></ul><ul><li>If down payment is 15% of total value of property, and </li></ul><ul><li>If net income of borrower is > $25,000, and </li></ul><ul><li>If employment is > 3 years at same company </li></ul><ul><li>If There are no previous credits problems </li></ul><ul><li> Then accept the applications </li></ul><ul><li>Else check other credit rules </li></ul>
  17. 17. Applications of Expert Systems DENDRAL: Used to identify the structure of chemical compounds. First used in 1965 MYCIN: Medical system for diagnosing blood disorders. First used in 1979
  18. 18. Applications of Expert Systems PROSPECTOR: Used by geologists to identify sites for drilling or mining PUFF: Medical system for diagnosis of respiratory conditions
  19. 19. Applications of Expert Systems LITHIAN: Gives advice to archaeologists examining stone tools DESIGN ADVISOR: Gives advice to designers of processor chips
  20. 20. Evolution of Expert Systems Software <ul><li>Expert system shell </li></ul><ul><ul><li>Collection of software packages & tools to design, develop, implement, and maintain expert systems </li></ul></ul>Ease of use low high Before 1980 1980s 1990s Traditional programming languages Special and 4 th generation languages Expert system shells
  21. 21. Need for expert systems <ul><li>1. Human expertise is very scarce. </li></ul><ul><li>2. Humans get tired from physical or mental workload. </li></ul><ul><li>3. Humans forget crucial details of a problem. </li></ul><ul><li>4. Humans are inconsistent in their day-to-day decisions. </li></ul><ul><li>5. Humans have limited working memory. </li></ul><ul><li>6. Humans are unable to comprehend large amounts of data quickly. </li></ul><ul><li>7. Humans are unable to retain large amounts of data in memory. </li></ul><ul><li>8. Humans are slow in recalling information stored in memory. </li></ul>
  22. 22. Expert System Limitations <ul><li>Limited focus </li></ul><ul><li>Inability to learn </li></ul><ul><li>Maintenance problems </li></ul><ul><li>May have high development costs </li></ul><ul><li>Can only solve specific types of problems in a limited domain of knowledge </li></ul>
  23. 23. Advantages <ul><li>Reduce employee training costs </li></ul><ul><li>Centralize the decision making process. </li></ul><ul><li>Create efficiencies and reduce the time needed to solve problems. </li></ul><ul><li>Combine multiple human expert intelligences </li></ul><ul><li>Reduce the amount of human errors. </li></ul><ul><li>Expert-system approaches provide the added flexibility </li></ul><ul><li>(Easy for modification) with the ability to model rules as data rather than as code </li></ul>
  24. 24. Problems with expert system <ul><li>Limited domain because , Domain experts cannot always clearly explain their logic and reasoning. </li></ul><ul><li>Systems are not always up to date, and </li></ul><ul><li>Don’t learn </li></ul><ul><li>Experts needed to setup and maintain system </li></ul><ul><li>No “common sense” , “ creative responses ” , give in unusual circumstances by humans. </li></ul><ul><li>Lack of flexibility and ability to adapt to changing environments </li></ul>
  25. 25. History <ul><li>Edward Albert Feigenbaum  is the &quot; Father of expert systems .“ </li></ul><ul><li>Mid-1960’s : Problem Solver (A. Newell and H.Simon ) </li></ul><ul><li>few laws of reasoning </li></ul><ul><li>+ </li></ul><ul><li>powerful computers = expertise performance (initial situation desired goal set of operators ) </li></ul><ul><li>Early Expert Systems (1970s) </li></ul><ul><ul><li>DENDRAL infers molecular structure from the unknown compounds </li></ul></ul><ul><ul><li>MYCIN medical diagnosing (bacterial infections of the blood ) </li></ul></ul>
  26. 26. “ A Hangover From AI Winter&quot; <ul><li>AI winter  Is a period of reduced funding and interest in  ARTIFICIAL INTELLIGENCE research. The process of HYPE , disappointment and funding cuts are common in many emerging technologies  </li></ul><ul><li>The worst times for AI were 1974−80 and 1987−93 </li></ul><ul><li>1966: The failure of  Machine Translation </li></ul><ul><li>1970: The abandonment of connectionism </li></ul><ul><li>1971−75: DARPA's frustration with the SPEECH UNDERSTANDING RESEARCH program at CARNEGIE MELLON UNIVERSITY </li></ul><ul><li>1973: In the United Kingdom LIGHTHILL REPORT </li></ul><ul><li>1973−74: DARPA's cutbacks to academic AI research in general, </li></ul><ul><li>1987: The collapse of the LISP MACHINE market, </li></ul><ul><li>1988: The cancellation of new spending on AI by the STRATEGIC COMPUTING INITIATIVE. </li></ul><ul><li>1993:  EXPERT SYSTEMS slowly reaching the bottom, </li></ul><ul><li>1990s: The quiet disappearance of the FIFTH GENERATION COMPUTER project's original goals. </li></ul>
  27. 27. Conclusion <ul><li>They require a lot of collaboration between a knowledge engineer and a domain expert. </li></ul><ul><li>When implemented correctly, to over come the human error expert systems remove </li></ul>
  28. 28. References: <ul><li>http://en.wikipedia.org/wiki/Expert_systems </li></ul><ul><li> http://www.exsys.com/demomain.html </li></ul><ul><li>http://machineslikeus.com/news/end-ai-winter </li></ul><ul><li>http://c2.com/cgi/wiki?AiWinter </li></ul>

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