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Introduction to AI

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Introduction to Artificial Intelligence

Introduction to Artificial Intelligence

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  • 1. INTELLIGENCE AND ARTIFICIAL INTELLIGENCE7/6/2012 Loganathan R 1
  • 2. INTRODUCTION • Machine intelligence is popularly known as Artificial Intelligence – AI • A.I. is the study of making computers smart – behaviour oriented view • A.I. is the study of making computer models of human intelligence - psychologists point of view • A.I. is the study concerned with building machines that simulate human behaviour - robotic approach • Thinking?... Television Air Conditioner Electric Cooker Washing Machine Automatic Iron Box Airplane Thirsty Crow ??????7/6/2012 Loganathan R 2
  • 3. KEEP IN MIND? • Data - a collection of disorganized facts like mutually unrelated numbers, characters, symbols etc. – For example frogs, flies, etc. • Information - an aggregation of data objects forming a syntactically correct structure. – For example frog flies. • Knowledge – a meaningful information. Hence, the above example is not contributing to knowledge. • Intelligence - the ability to understand, apply and acquire the knowledge • Artificial - Made as a copy of something natural7/6/2012 Loganathan R 3
  • 4. DEFINITION BY ELIANE RICH• Artificial Intelligence is the study of how to make computers do things, at which, at the moment, people are better• Some Tasks (numerical computation, information storage, repetitive tasks, etc) that computers can do better than human• Some Tasks (understanding, predicting, common-sense reasoning , conclusions on incomplete information, etc i.e. requires parallel processing and simultaneous availability) that human can do better than computers beings7/6/2012 Loganathan R 4
  • 5. DEFINITION BY BUCHANIN AND SHORTLIFFE• AI is the branch of computer science that deals with symbolic rather than numeric processing and non- algorithmic methods including the rules of thumb or heuristics instead of algorithms as techniques for solving problems• In numeric processing only a small number of well-defined relations and operations• In symbolic processing the relations and operations required to solve a problem depend upon the problem under consideration• Non-algorithmic method - rule of thumb that may apply to the current problem, it may suggest to us how to proceed• Heuristics experience-based techniques for problem solving, learning, and discovery7/6/2012 Loganathan R 5
  • 6. ANOTHER DEFINITION BY ELIANE RICH• Artificial Intelligence is the study of techniques for solving exponentially hard problems in polynomial time exploiting knowledge about the problem domain• Polynomial time is a reasonable amount of time• Exponential time a impractical or infeasible amount of time7/6/2012 Loganathan R 6
  • 7. DEFINITION BY BARR AND FEIGENBAUM• Artificial Intelligence is the part of computer science concerned with designing intelligent computer systems, i.e., systems that exhibit the characteristics we associate with intelligence in human behaviour DEFINITION BY SHALKOFF• Perhaps broadest definition is that AI is a field of study that seeks to explain and emulate intelligent behaviour in terms of computational processes7/6/2012 Loganathan R 7
  • 8. TESTING AI?• In 1950, Alan Turing proposed the following method for determining whether a machine can think. Here we used three rooms A, B & C. In A&B we can keep a machine and a human. In room C a human interrogator is kept.• If the human interrogator in room C is not able to identify who is in room A and B, then the machine possesses intelligence7/6/2012 Loganathan R 8
  • 9. Difference? Dimension Conventional Computing Intelligent Computing Processing Algorithmic Includes conceptualizations Nature of Input Must be complete Can be complete Search Approach Based on algorithms Based on rules & Heuristics Explanation Not provided Provided Focus Data, Information Knowledge Maintenance &Update Usually Difficult Relatively easy Reasoning capability No Yes AI programs Conventional programs Symbolic processing Numeric processing It involves large knowledge base Large data base Modifications are frequent Modifications are rare Heuristic search technique is used Algorithms search technique is used Solutions steps are not explicit Solution steps are precise Knowledge is imprecise Knowledge is precise7/6/2012 Loganathan R 9
  • 10. APPLICATION AREAS• Reasoning and decision making(chess, general games, industrial scheduling, etc)• Knowledge Representation and Reasoning (logical, probabilistic)• Decision Making (search, planning, decision theory)• Machine Learning (Google engine)• Computer vision (face/scene recognition)• Natural language processing (recognition, translation)• Robotics (Mars rover, urban challenge, Robocup)7/6/2012 Loganathan R 10
  • 11. Puzzled?7/6/2012 Loganathan R 11
  • 12. 7/6/2012 Loganathan R 12
  • 13. 7/6/2012 Loganathan R 13

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