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授課教師：顏士淨 2013/09/12

AI. 授課教師：顏士淨 2013/09/12. Part I & Part II.

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授課教師：顏士淨 2013/09/12

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  1. AI 授課教師：顏士淨 2013/09/12

  2. Part I & Part II • Part I Artificial Intelligence          1 Introduction           2 Intelligent Agents Part II Problem Solving          3 Solving Problems by Searching           4 Beyond Classical Search           5 Adversarial Search           6 Constraint Satisfaction Problems

  3. Part III • Part III Knowledge and Reasoning      7 Logical Agents 8 First-Order Logic 9 Inference in First-Order Logic 10 Classical Planning 11 Planning and Acting in the Real World 12 Knowledge Representation

  4. Part IV • Part IV Uncertain Knowledge and Reasoning      13 Quantifying Uncertainty 14 Probabilistic Reasoning 15 Probabilistic Reasoning over Time 16 Making Simple Decisions 17 Making Complex Decisions

  5. Part V Learning • Part V Learning • 18 Learning from Examples 19 Knowledge in Learning 20 Learning Probabilistic Models 21 Reinforcement Learning

  6. Part VII && Part VIII • Part VII Communicating, Perceiving, and Acting        22 Natural Language Processing         23 Natural Language for Communication         24 Perception         25 Robotics Part VIII Conclusions

  7. What is AI? • Systems that… • Thinking humanly? • Thinking rationally? • Acting humanly? • Acting rationally?

  8. Thinking Humanly: Cognitive Science • 1960s ”cognitive revolution” • Information-processing psychology replaced prevailing orthodoxy of behaviorism • Requires scientific theories of internal activities • How to validate? Requires • Predicting and testing behavior of human subjects(top-down) • Direct identification from neurological data(bottom-up)

  9. Thinking Rationally: Laws of Thought • Aristotle: • what are correct arguments/thought processes? • Logic: • notation and rules of derivation for thoughts • Direct line thought mathematics and philosophy to modern AI

  10. Problems • Not all intelligent behavior is medicated by logical deliberation • What is the purpose of thinking?

  11. Acting Humanly: The Turing Test(1/2) • Turing(1950) “Computing machinery and intelligence” • “Can machines think?””Can machines behave intelligently?” • Operation test for intelligent behavior:

  12. Acting Humanly: The Turing Test(2/2) • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated all major arguments against AI in following 50 years • Suggested major components of AI: knowledge, reasoning, language understanding, learning • Watson

  13. Acting Rationally • Rational behavior: doing the right thing • The right thing • That which is expected to maximize goal achievement, given the available information • Aristotle: Every art and every inquiry, and similarly every action and pursuit, is thought to aim at some good

  14. Rational Agents • An agent is an entity that perceives and acts • This course is about designing rational agents • Abstractly, an agent is a function from percept histories to actions: f: P*A • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance • Caveat: computational limitations make perfect rationality unachievable design best program for given machine resources

  15. AI prehistory(1/3) • Philosophy(428 B.C.-) logic, methods of reasoning mind as physical system foundations of learning, language, rationality • Mathematics(800 B.C. -) Formal representation and proof algorithms, computation, (un) decidability probability

  16. AI prehistory(2/3) • Economics(1766-) formal theory of rational decisions Decision theory Game theory • Neuroscience(1861-) plastic physical substrate for mental activity • Psychology(1879-) adaptation cognitive science

  17. AI prehistory(3/3) • Computer engineering(1940-) • Control theory homeostatic systems, stability simple optimal agent designs • Linguistics knowledge representation grammar natural language processing

  18. Potted history of AI(1/3) 1943 McCulloch&Pitts: Boolean circuit model of brain 1950 Turing’s “Computing Machinery and Intelligence: 1950s Early AI programs, including Samuel’s checkers program, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine 1956 Dartmouth meeting: “Artificial Intelligence” adopted

  19. Potted history of AI(2/3) 1965 Robinson’s complete algorithm for logical reasoning 1966-74 AI discovers computational complexity, Neural network research almost disappears 1969-79 Early development of knowledge-based systems 1980-88 Expert systems industry booms 1988-93 Expert systems industry busts: “AI Winter”

  20. Potted history of AI(3/3) 1985-95 Neural networks return to popularity 1988- Resurgence of probability; general increase in technical depth “Nouvelle AI”: ALife, GAs, soft computing 1995- Agents agents every where

  21. State of the art • Autonomous planning and scheduling • Game playing • Autonomous control • Diagnosis • Logistics planning • Robotics • Language understanding and problem solving

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