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Artificial Intelligence Overview

Dive into the world of AI with Professor John Paxton's insightful overview covering the foundations, history, areas, and more. Learn about different approaches, agent types, and key concepts in AI development.

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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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