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Artificial Intelligence Overview

Artificial Intelligence Overview. John Paxton Montana State University February 22, 2005 paxton@cs.montana.edu. Montana State University. A Brief Bio. 1985: The Ohio State University, B.S. 1987: The University of Michigan, M.S. 1990: The University of Michigan, Ph.D.

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Artificial Intelligence Overview

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  1. Artificial Intelligence Overview John Paxton Montana State University February 22, 2005 paxton@cs.montana.edu

  2. Montana State University

  3. A Brief Bio • 1985: The Ohio State University, B.S. • 1987: The University of Michigan, M.S. • 1990: The University of Michigan, Ph.D. • 1990 – present: MSU CS Professor

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

  5. What is AI? Scientific Approach • Build systems that think like humans • Build systems that act like humans Engineering Approach • Build systems that think rationally • Build systems that act rationally

  6. Acting Like a Human • Turing Test (1950) IBM

  7. Thinking Like a Human • Cognitive Modeling Approach • General Problem Solver (Newell and Simon, 1961) • Towers of Hanoi Problem

  8. Thinking Rationally • The laws-of-thought approach • Syllogisms (Aristotle): deductive reasoning in which a conclusion is derived from premises • It is difficult to code the knowledge and to reason with it efficiently.

  9. Sample Logic Puzzle • Robinson found himself on an island where some of the people were liars, and others always told the truth. When he met with one of the inhabitant of the island, he asked him: "Are you a liar or not?""I'm not a liar", answered the person."All right, if it is so, you'll be my companion", Robinson said.After a while they saw another man.Robinson pointed to the man and asked his new friend,"Could you, please, ask him, if he is a liar or not?"The new friend asked the question to the man, came back and said,"He said he was not a liar"."All right, now I'm convinced that you are not a liar!" smiled Robinson. What convinced Robinson?

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

  11. Water Jug Problem 4 10 8 5 2 15 5 10 4

  12. 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?) • 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?)

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

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

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

  16. Search • Missionaries and Cannibals Problem MMM CCC

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

  18. Types of Search • Uninformed Search • Breadth-First Search • Depth-First Search • Informed Search • Best-First Search • A* Search

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

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

  21. Supply the Missing Words! • 60 = M in an H • 26 = L in the A • 12 = S of the Z • 88 = P K • 200 = D for P G in M

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

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

  24. 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)

  25. Agents sensors actuators AGENT ENVIRONMENT

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

  27. 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.

  28. Thank you! • Questions??

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