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Artificial Intelligence Overview

Artificial Intelligence Overview. John Paxton Montana State University August 14, 2003. Montana State University. A Brief Bio. 1985 The Ohio State University, B.S. Computer Science 1987 The University of Michigan, M.S. Computer Science

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Artificial Intelligence Overview

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  1. Artificial Intelligence Overview John Paxton Montana State University August 14, 2003

  2. Montana State University

  3. A Brief Bio • 1985 The Ohio State University, B.S. Computer Science • 1987 The University of Michigan, M.S. Computer Science • 1990 The University of Michigan, Ph.D. Artificial Intelligence • 2003 Montana State University – Bozeman, Professor of Computer Science

  4. Talk Outline • What is AI? • Foundations • History • Areas • Search • Knowledge Representation • Agents

  5. What is AI? Science Approach • Systems that think like humans • Systems that act like humans Engineering Approach • Systems that think rationally • Systems that act rationally

  6. Acting Humanly • Turing Test (1950)

  7. Thinking Humanly • Cognitive Modelling Approach • General Problem Solver (Newell and Simon, 1961)

  8. Thinking Rationally • The laws-of-thought approach • Syllogisms (Aristotle) • It is difficult to code the knowledge and to reason with it efficiently.

  9. Acting Rationally • Rational Agent Approach. The agent acts to achieve the best (or near best) expected outcome.

  10. Foundations • Philosophy (e.g. Where does knowledge come from?) • Mathematics (e.g. What are the formal rules to draw valid conclusions?) • Economics (e.g. How should we make decisions to maximize payoff?) • Neuroscience (e.g. How do brains process information?)

  11. Foundations • Psychology (e.g. How do humans and animals think and act?) • Computer Engineering (e.g. How can we build an efficient computer?) • Control Theory (e.g. How can artifacts operate under their own control?) • Linguistics (e.g. How does language relate to thought?)

  12. History • 1943-1955 Gestation. McCulloch-Pitts, Hebb, Turing Test • 1956. Dartmouth Conference. • 1952-1969. Great Expectations. Logic Theorist, GPS, Checkers, Lisp, Microworlds (calculus) • 1966-1973. Reality. Machine translation (spirit == vodka), chess, intractability, fundamental limitations (Perceptrons).

  13. History • 1969-1979. Knowledge-Based Systems. Dendral (infer molecular structure) • 1980-present. Commercial Products. • 1986-present. Return of neural networks. • 1987-present. Science. Hidden Markov Models. Neural Networks. Bayesian Networks. • 1995-present. Intelligent Agents.

  14. Areas • Agents • Artificial Life • Machine Discovery and Data Mining • Expert Systems • Fuzzy Logic • Game Playing • Genetic Algorithms

  15. Areas • Knowledge Representation • Learning • Neural Networks • Natural Language Processing • Planning • Reasoning • Robotics

  16. Areas • Search • Speech Recognition and Synthesis • Virtual Reality • Computer Vision

  17. Search • Missionaries and Cannibals Problem MMM CCC

  18. Search • Missionaries and Cannibals Solution M C MM CC MMM CCC MMM CCC MMM CC C M C MMM C MM CC MM CC MMM C M C CC CC

  19. Types of Search • Blind Search • Breadth-First Search • Depth-First Search • Informed Search • Best-First Search • A* Search

  20. Breadth-First Search MMM CCC MMM C MM CC M C MMM CC CC C

  21. Minimax Search • Commonly used to determine which move to make in a 2 player, strategy game. • Deep Junior (Ban, Bushinsky, Alterman), the reigning computer chess champion uses minimax. • Minimax requires an evaluation function.

  22. Minimax Example • Nim 4 (my move) 3 2 1 (your move) 2 1 11 (my move) 1 (your move)

  23. Chess Example maximizer * minimizer * * * 3 0 -5 4 10 2

  24. Knowledge Representation • Semantic Nets • Fuzzy Logic • First Order Predicate Calculus

  25. Semantic Nets can-fly yes bird is-a is-a is-a no robin magpie ostrich can-fly

  26. Fuzzy Logic • Shaquille O’Neal is tall 1.0 0.0 tall 5’0 6’0 7’0

  27. Fuzzy Logic • Karim is tall (0.6) and a good teacher (0.9) = 0.6 • Karim is tall or a good teacher = 0.9. • Karim is not tall = 1.0 – 0.6 = 0.4

  28. First Order Predicate Calculus • Every Saturday is a weekend.x Saturday(x)  weekend(x) • Some day is a week day.x day(x)  weekday(x)

  29. Agents sensors actuators AGENT ENVIRONMENT

  30. Rationality Factors • Performance Measure • Prior Knowledge • Performable Actions • Agent’s Prior Percepts

  31. Rational Agent • For each possible sensor sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the sensor sequence and whatever built-in knowledge the agent has.

  32. Agent Terminology • Omniscience: the outcome of its actions are known. Impossible! • Learning: taking actions in order to perform better (e.g. robot vacuum cleaner) • Autonomy: the agent relies on its own sensors rather than built-in knowledge

  33. Environments • Fully observable vs. partially observable • Deterministic vs. stochastic • Episodic (classification) vs. sequential (conversation) • Static vs. dynamic • Discrete (chess) vs. continuous (taxi-driving) • Single agent vs. multi-agent.

  34. Types of Agents • Reflex • Model-Based • Goal-Based • Utility-Based • Learning • Combinations of the above!

  35. Questions?

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