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Artificial Intelligence and Expert Systems

HFE 451/651 Artificial Intelligence and Expert Systems -Presented By Damodar Kavya Sogra Contents Introduction Definitions of AI Approaches of AI History of AI Designing an AI system Applications of AI Expert Systems Conclusion References Questions???? Introduction

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Artificial Intelligence and Expert Systems

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  1. HFE 451/651 Artificial Intelligence and Expert Systems -Presented By Damodar Kavya Sogra

  2. Contents Introduction Definitions of AI Approaches of AI History of AI Designing an AI system Applications of AI Expert Systems Conclusion References Questions????

  3. Introduction Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think".

  4. Introduction(contd.) AI researchers are active in a variety of domains. Formal Tasks (mathematics, games), Mundane tasks (perception, robotics, natural language, common sense reasoning) Expert tasks (financial analysis, medical diagnostics, engineering, scientific analysis, and other areas)

  5. Some definitions of AI

  6. Approaches to AIActing humanly: The Turing Test approach • Alan Turing(1950) • Designed to provide a satisfactory operational definition of intelligence • Intelligent behavior- The ability to achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator. The computer would need to possess • Natural language processing • Knowledge representation • Automated reasoning • Machine learning

  7. Thinking humanly: The Cognitive modelling approach Determine how humans think • Introspection • Psychological experiment Come up with precise theory of the mind and express as a computer program • GPS - Newall and Simon, 1961 • Wang

  8. Thinking rationally: The laws of thought approach • Aristotle – “Right thinking” • Laws of thought govern the operation of mind – initiated the field of logic • Programs based on laws of thought to create intelligent systems Main obstacles • Informal knowledge in terms of formal terms - Difference between theoretical and practical approach

  9. Acting rationally: The rational agent approach • Acting so as to achieve one’s goals given one’s beliefs • Agent – perceives and acts • AI is the study and construction of agents • Situational awareness unlike the laws of thought approach(makes inferences) • Knowledge and reason to reach good decisions in a wide variety of situations Advantages: • More general than laws of thought approach - More open to scientific development than approaches based on human behavior or thought –clearly defined rationality

  10. Why Artificial Intelligence?? • Attempts to understand intelligent entities-learn more about ourselves • Strives to build intelligent entities as well as understand them • Computers with human-level intelligence(or better) would have a huge impact on our daily life • Allows less or no human involvement

  11. History of AI The beginnings of AI reach back before electronics, to philosophers and mathematicians such as Boole and others theorizing on principles that were used as the foundation of AI Logic. AI really began to intrigue researchers with the invention of the computer in 1943 The technology was finally available, or so it seemed, to simulate intelligent behavior

  12. History of AI • Warren McCulloch and Walter Pitts (1943) developed a model of artificial neurons. • Claude Shannon (1950), and Alan Turing (1953) developed chess programs • John McCarthy, Marvin Minsky, Shannon and Nathaniel Rochester - neural networks and the study of intelligence

  13. History of AI • A big contribution to AI, again came from McCarthy in 1958 when he wrote a high level programming language called 'LISP'. • Allen Newell and Herbert Simon developed 'General Problem Solver‘ • Weizenbaum's ELIZA program (1965) • MYCIN was developed to diagnose blood infections. • Many other algorithms

  14. History of AI(contd.) AI has grown from a dozen researchers, to thousands of engineers and specialists; and from programs capable of playing checkers, to systems designed to diagnose disease. Advanced-level computer languages, as well as computer interfaces and word-processors owe their existence to the research into artificial intelligence.

  15. Designing an AI System Top Down Approach 2. Bottom Up Approach Bottom Up Approach is most widely used

  16. Some Facts about the Human Brain • Human Brain is made up of Billions of cells called neurons • Neurons work when grouped together • Decisions are made by passing electrical signals • Neurons are devices for processing Binary digits

  17. How Binary processing works • Binary numbers are represented as 0 and 1or T and F • A decision is made from a given input in terms of 0 and 1 • Apples are red-- is True • Apples are red AND oranges are purple-- is False • Apples are red OR oranges are purple-- is True • Apples are red AND oranges are NOT purple-- is also True

  18. Relevance to the Human Mind • The Human Mind works on the principle of Binary processing • Information is transmitted via impulses • Presence of impulse – True • Absence of impulse –False *Logical Operation is based on two or more such signals

  19. Network of Neurons

  20. Decision Making Process • Identify a Bird

  21. Applications Of AI • Banking System - Micro Bankers High Tech Banking System - Internet Banking • Medicine - MYCIN - INTERNEST • Eliza - The Psychotherapist

  22. ELIZA- computer therapist http://www.manifestation.com/neurotoys/eliza.php3

  23. Expert Systems Expert systems are computerized advisory programs that attempt to imitate the reasoning process and knowledge of experts in solving specific types of problems.

  24. History 1960s 1970s Renaissance Age

  25. What can Expert Systems do? Diagnosis Instruction Monitoring Analyzing Interpretation Debugging Repair Control Consulting Planning Design

  26. Knowledge Engineering-the discipline of building expert systems Knowledge Acquisition Knowledge Elicitation Knowledge Representation

  27. How does it work? Knowledge Base Inference Engine A generalized Interface

  28. When Expert Systems are applicable to the Nature of the task? Expert systems can do much better Task involves reasoning and knowledge and not intuition or reflexes Task can be done in minutes or hours Task is concrete enough to codify The task is commonly taught to novice in the area.

  29. When expert systems are applicable Nature of the knowledge Recognized expert exist There is general agreement among experts Experts are able and willing to articulate the way they approach problems.

  30. How the system works? Use AI techniques Knowledge component Separate knowledge and control Use inference procedures - heuristics - uncertainty Model human expert

  31. Comparison of conventional and expert systems Conventional System Expert System Information and processing are Knowledge base is separated from processing combined in one program mechanism May make mistakes Does not make mistakes Changes are tedious Changes are easy System operates only when completed System can operate even with few rules Data processing is a repetitive process Knowledge engineering is inferential process Algorithmic Heuristic Representation and use of data Representation and use of knowledge

  32. How do people reason? They create categories They use specific rules, a priori rules They Use Heuristics --- "rules of thumb" They use past experience --- "cases" They use "Expectations"

  33. How do Computers Reason? Computer models are based on models of human reasoning They use rules A--->B--->C They use cases They use pattern recognition/expectations

  34. Features of Expert Systems Deal with complex subject which normally require a considerable amount of human expertise. Exhibit performance and high reliability Capable of explaining and justifying solutions and recommendations.

  35. Features of Expert Systems(contd.) • Incorporate some form of Inferential reasoning. • Be flexible, capable of accomodating significant changes without necessary programming • Be user friendly

  36. Examples of Expert Systems • Dendral-Identify organic compounds. • Mycin-diagnosing medical problems. • Prospector-identifying mineral deposits • XCON-customized hardware configuration. • Expert Tax- accrual and tax planning

  37. Advantages of Expert Systems Permanence Reproducibility Efficiency Consistency Documentation Completeness Timeliness Differentiation

  38. Disadvantages of Rule-Based Expert Systems Creativity Learning Sensory Experience Degradation Common sense

  39. Conclusion: Computers Think--and Often Think Like People

  40. References • Artificial Intelligence – A Modern Approach-Stuart J. Russell and Peter Norvig • http://library.thinkquest.org • http://www.ai.mit.edu/people/minsky/minsky.html • What is Artificial Intelligence? by John McCarthy, Computer Science Department, Stanford University • What is Artificial Intelligence? by Aaron Sloman, Computer Science Department, University of Birmingham, UK • Expert Systems: A Quick Tutorial - by Schmuller, Dr. Joseph, Journal of Information Systems Education 9/92, Volume 4, Number 3 • Artificial Intelligence a Modern Approach --- Chapter 1 Introduction by Stuart Russell and Peter Norvig. • AI Tutorial by Eyal Reingold, University of Toronto • AI Education Repository - links to classes, tutorials etc.

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