Artificial Intelligence Overview John Paxton Montana State University February 22, 2005

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 – present: MSU CS Professor

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

What is AI? Scientific Approach 1.Build systems that think like humans 2.Build systems that act like humans Engineering Approach 1.Build systems that think rationally 2.Build systems that act rationally

Acting Like a Human Turing Test (1950) IBM

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

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.

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?

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

Water Jug Problem

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?)

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

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

Areas Search Speech Recognition and Synthesis Virtual Reality Computer Vision

Search Missionaries and Cannibals Problem MMM CCC

Search Missionaries and Cannibals Solution MMM CCC MM CC MCMC CMMM CC MMM CCC MMM C CC MCMC MM CC MM CC MCMC MMM C

Types of Search Uninformed Search –Breadth-First Search –Depth-First Search Informed Search –Best-First Search –A* Search

Breadth-First Search MMM CCC MMM CC C MMM C CC MM CC MCMC

Knowledge Representation Semantic Nets Fuzzy Logic First Order Predicate Calculus

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

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

Fuzzy Logic Shaquille O’Neal is tall 5’0 6’0 7’0 tall

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)

Agents AGENT ENVIRONMENT sensors actuators

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

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.

Thank you! Questions??