Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Expert Systems

5 754 vues

Publié le

Expert Systems and Artificial Intelligence

Publié dans : Formation
  • Soyez le premier à commenter

Expert Systems

  1. 1. 1. Artificial Inteligence Hande TETİK 2. Expert Systems Aslı YAZAĞAN Hande TETİK IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  2. 2. Overview Artificial Intelligence Field Concepts in ES – Expert and Expert Systems Structure of Expert Systems How Expert Systems work Categories of Expert Systems Knowledge – Based Systems vs. Expert Systems Expert Systems Success Factor Types of Expert Systems Benefit of Expert Systems Problem and Limitations of Expert Systems Expert Systems on the WEB ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  3. 3. Artificial intelligence (AI) A subfield of computer science, concerned with symbolic reasoning and problem solving AI has many definitions… Behavior by a machine that, if performed by a human being, would be considered intelligent “…study of how to make computers do things at which, at the moment, people are better Theory of how the human mind works IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE
  4. 4. AI pioneers • Regarded as a father of AI • The Darthmouth summer research project on AI (1956) • «Making a machine behave in ways that would be called intelligent if a human were so behaving» IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE
  5. 5. AI Objectives • Make machines smarter (primary goal) • Understand what intelligence is • Make machines more intelligent and useful Signs of intelligence • Learn or understand from experience • Make sense out of ambiguous situations • Respond quickly to new situations • Use reasoning to solve problems • Apply knowledge to manipulate the environment IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE Questions / Answers
  6. 6. Symbolic Processing Represents knowledge as a set of symbols, and AI Uses these symbols to represent problems, and Apply various strategies and rules to manipulate symbols to solve problems A symbol is a string of characters that stands for some real-world concept (e.g., Product, consumer,…) Examples: (DEFECTIVE product) (LEASED-BY product customer) - LISP Tastes_Good (chocolate) IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE
  7. 7. AI Concepts Reasoning Inferencing from facts and rules using heuristics or other search approaches Pattern Matching Attempt to describe and match objects, events, or processes in terms of their qualitative features and logical and computational relationships Knowledge Base IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE Computer Inference Capability Knowledge Base INPUTS (questions, problems, etc.) OUTPUTS (answers, alternatives, etc.)
  8. 8. Evolution of artificial intelligence IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE Time ComplexityoftheSolutions Naïve Solutions General Methoids Domain Knowledge Hybrid Solutions Embedded Applications 1960s 1970s 1980s 1990s 2000+ Low High
  9. 9. Artificial vs. Natural Intelligence Advantages of AI  More permanent  Ease of duplication and dissemination  Less expensive  Consistent and thorough  Can be documented  Can execute certain tasks much faster  Can perform certain tasks better than many people Advantages of Biological Natural Intelligence  Is truly creative  Can use sensory input directly and creatively  Can apply experience in different situations IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE
  10. 10. AI Field  Provides the scientific foundation for many commercial technologies  AI is many different sciences and technologies  It is a collection of concepts and ideas IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE Psychology Philosophy Logic Sociology Human Cognition Linguistics Neurology Mathematics Management Science Information Systems Statistics Engineering Robotics Biology Human Behavior Pattern Recognition Voice Recognition Intelligent tutoring Expert Systems Neural Networks Natural Language Processing Intelligent Agents Fuzzy Logic Game Playing Computer Vision Automatic Programming Genetic Algorithms Machine Learning Autonomous Robots Speech Understanding The AI Tree Computer Science DisciplinesApplications
  11. 11. AI Areas Major • Expert Systems • Natural Language Processing • Speech Understanding • Robotics and Sensory Systems • Computer Vision and Scene Recognition • Intelligent Computer-Aided Instruction • Automated Programming • Neural Computing Game Playing Additional • Game Playing, Language Translation • Fuzzy Logic, Genetic Algorithms • Intelligent Software Agents IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE
  12. 12. IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE • AI is a common technology both science fiction and projections about the future of technology& society • The impact of AI on society is a serious area of study for futurists ROBOCOP FRIEND
  13. 13. 1. Artificial Inteligence Hande TETİK 2. Expert Systems Aslı YAZAĞAN Hande TETİK IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  14. 14. What is an Expert ? An expert is one has ability to use skill experience knowledge efficiently to solve a problem using tricks, shortcuts, and rules-of-thumb. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  15. 15. Which one is an Expert on web search? 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  16. 16. Who knows the best treatment for you? 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  17. 17. Who is an Expert on music? 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  18. 18. Which one is an Expert on cooking? 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  19. 19. Consulting and Consultants What are the attributes of effective consultants and consulting? Consulting is goal oriented A good consultant is efficient Good consultants justify their recommendations by explaining their reasoning Consultants are able to work with imperfect information A consultation is adaptive 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  20. 20. Consultants and Consulting Example 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan GOAL ORIENTED EFFICIENT
  21. 21. IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 2. EXPERT SYSTEMS ADAPTIVE IMPERFECT INFORMATION EXPLAIN REASONING
  22. 22. Expert Systems - Expert systems represent a practical application of artificial intelligence (AI) research. - Attempts to imitate expert’s reasoning processes and knowledge in solving specific problems - ES do not replace experts. Make their knowledge and experience more widely available, Permit non-experts to work better 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1.Generation • If-then rules 2.Generation • Flexible in adopting multiple knowledge representation and reasoning method ( Neural Network )
  23. 23. Why Expert Systems?  A tool for preserving the professional knowledge that is crucial to competitiveness  A tool for documenting professional knowledge for examination or improvement  A tool for training new employees and disseminating knowledge  A tool to transfer knowledge more easily at lower cost Transfering Expertise  acquisition  representation  inferencing  transfer 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  24. 24. Structure of Expert Systems 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan Knowledge Engineer Human Expert(s) Other Knowledge Sources Knowledge Elicitation Information Gathering Knowledge Base(s) (Long Term) Rule Firings Knowledge Rules Inferencing Rules Data-information predictions relations-consequences
  25. 25. Structure of Expert Systems | Inference Engine The brain of ES system. Interprets rules and draw conclusions. Inference is the process of chaining multiple rules together based on available data - If the expert first collect data then infer from it => Forward Chaining - If the expert starts with a hypothetical solution and then attempts to find facts to prove it => Backward Chaining 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan  Forward Chaining • Data driven • When: If all facts available up front. • If  then  Backward Chaining • Goal Driven • When: there are many attributes employed in many rules (e.g diagnostic problems ) • Then  If
  26. 26. Inference Engine| Forward Chaning Example 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan QUESTION: What is the diagnosis? • R2 fires, adding (nasal congestion) to working memory. • R4 fires, adding (fever) to working memory. • R5 fires, adding (achiness) to working memory. • R6 fires, adding (viremia) to working memory. • R1 fires, diagnosing the disease as (influenza) and exits, returning the diagnosis Source: http://ai-depot.com/Tutorial/RuleBased-Conclusion.html
  27. 27. Inference Engine | Backward Chaning Example 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan QUESTION: Is the diagnosis Influenza ? • R1 fires since the goal, diagnosis(influenza), matches the conclusion of that rule. New goals are created: (nasal congestion) and (viremia) and backchaining is recursively called with these new goals. • R2 fires, matching goal nasal congestion. New goal is created: (runny nose). Backchaining is recursively called. Since (runny nose) is in working memory, it returns true. • R6 fires, matching goal viremia. Back-chaining recursion with new goals: (fever), (achiness) and (cough) • R4 fires, adding goal (temperature > 100). Since (temperature = 101.7) is in working memory, it returns true. • R3 fires, adding goal (body-aches). On recursion, there is no information in working memory nor rules that match this goal. Therefore it returns false and the next matching rule is chosen. That rule is R5 which fires, adding goal (headache). Since (headache) is in working memory, it returns true. • Goal (cough) is in working memory, so that returns true. • Now, all recursive procedures have returned true, the system exits, returning true: this hypothesis was correct: subject has influenza. Source: http://ai-depot.com/Tutorial/RuleBased-Conclusion.html
  28. 28. Categories for Expert Systems 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan Category Problem Adressed Interpretation Inferring situation descriptions from observations Prediction Inferring likely consequences of given situation Diagnosis Inferring system malfunctions from observation Design Configuring objects under constraints Planning Developing plans to achieve the goals Monitoring Comparing observations to plans, flagging exceptions Debugging Prescribing remedies for malfunctions Repair Executing a plan to administer a remedy Instruction Diagnosing, debugging, and correcting student performance Control Interpreting, predicting, repairing and monitoring system behaivors
  29. 29. Knowledge Based Systems vs. Expert Systems • Expert system makes decision and solves problems using knowledge and analytical rules defined by experts in that field. • Knowledge System performs tasks using knowledge that do not really need an expert. Can be constructed more quickly and cheaply than Expert Systems. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan Knowledge Base Expert Systems
  30. 30. Expert Systems Success Factor  The task must be clearly defined.  Test case examples should be available. Expert systems are built to solve problems that have been solved before: Documented test cases will provide a list of the factor present each time the problem was solved along with the solution. The advising task should have a verbal orientation. If the problem environment requires extensive visual reference and graphical information that cannot be described verbally, it might very difficult to implement an expert system e.g : architecture 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  31. 31. Types of Expert Systems 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan Rule-based ES. Knowledge is represented by a series of rules. Frame-based Systems. Knowledge is represented as a series of frames (an object-oriented approach). Hybrid Systems. Involve several approaches such as fuzzy logic and neural networks. Model-based Systems. Structured around a model that simulates the structure and function of the system under study. Ready-made Systems. Utilize prepackaged software. Real-time Systems. Systems designed to produce a just-in-time response.
  32. 32. Benefits of Expert Systems  Capture Scarce Expertise  Increased Productivity and Quality  Decreased Decision Making Time  Reduced Downtime via Diagnosis  Easier Equipment Operation  Elimination of Expensive Equipment  Ability to Solve Complex Problems  Knowledge Transfer to Remote Locations  Integration of Several Experts' Opinions  Can Work with Uncertain Information  Etc. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  33. 33. Problems and Limitations of ES  Knowledge is not always readily available  Expertise can be hard to extract from humans • Fear of sharing expertise • Conflicts arise in dealing with multiple experts  ES work well only in a narrow domain of knowledge  Experts’ vocabulary often highly technical  Knowledge engineers are rare and expensive  Lack of trust by end-users  ES sometimes produce incorrect recommendations 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan
  34. 34. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan ES Critical Success Factors • Having a Champion in Management • User Involvement and Training • Justification of the Importance of the Problem • Good Project Management • The level of knowledge must be sufficiently high • There must be (at least) one cooperative expert • The problem must be mostly qualitative • The problem must be sufficiently narrow in scope • The ES shell must be high quality, with friendly user interface, and naturally store and manipulate the knowledge
  35. 35. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan • Only about 1/3 survived more than five years • Generally ES failed due to managerial issues – Lack of system acceptance by users – Inability to retain developers – Problems in transitioning from development to maintenance (lack of refinement) – Shifts in organizational priorities • Proper management of ES development and deployment could resolve most of them Longevity of Commercial ES
  36. 36. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan Applications of Expert Systems DENDRAL: Used to identify the structure of chemical compounds. First used in 1965 LITHIAN: Gives advice to archaeologists examining stone tools
  37. 37. IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE PROSPECTOR: Used by geologists to identify sites for drilling or mining PUFF: Medical system for diagnosis of respiratory conditions Applications of Expert Systems
  38. 38. Expert Systems on the Web 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan http://www.aiinc.ca/http://www.vanguardsw.com http://www.expertise2go.com
  39. 39. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan An ES Consultation with ExSys • Founded in 1983 • Longest-lived knowledge automation expert system software company in the industry ExSys
  40. 40. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan Example of Application Areas: Dog Breeding Advisor Considers Various Factors • Suitability with small children • Exercise and grooming requirements • Look or size of the dog Results
  41. 41. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan Exsys Corvid Case Studies
  42. 42. 2. EXPERT SYSTEMS IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan • Transportation waterways and streams are important • Preventation of destructive erosion, scour, and lateral migration taken into account • Salix Applied Earthcare used Exsys Corvid®to develop a knowledge automation expert system named Greenbank to address this need. • 44 channel and bank protection procedures were identified and incorporated into the Exsys Corvid system, which recommends the best techniques for particular situations.
  43. 43. Who is responsible if the advice is wrong? • The user? • The domain expert? • The knowledge engineer? • The programmer of the expert system shell? • The company selling the software? IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan 1. ARTIFICIAL INTELLIGENCE Legal and Ethical Issues
  44. 44. Thank you!

×