Artificial Intelligence Overview - Montana State University
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
1990 The University of Michigan, Ph.D. Artificial Intelligence 2003 Montana State University Bozeman, Professor of Computer Science Talk Outline
What is AI? Foundations History Areas Search Knowledge Representation Agents What is AI? Science Approach
1. Systems that think like humans 2. Systems that act like humans Engineering Approach 1. Systems that think rationally 2. Systems that act rationally Acting Humanly Turing Test (1950) Thinking Humanly Cognitive Modelling Approach General Problem Solver (Newell and
Simon, 1961) Thinking Rationally The laws-of-thought approach Syllogisms (Aristotle) It is difficult to code the knowledge and to reason with it efficiently. Acting Rationally Rational Agent Approach. The agent acts to achieve the best (or near best) expected outcome.
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?) 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?) History 1943-1955 Gestation. McCulloch-Pitts, Hebb, Turing Test
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 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
Performable Actions Agents 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. 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 Environments Fully observable vs. partially observable Deterministic vs. stochastic Episodic (classification) vs. sequential (conversation)
Static vs. dynamic Discrete (chess) vs. continuous (taxidriving) Single agent vs. multi-agent. Types of Agents
Reflex Model-Based Goal-Based Utility-Based Learning Combinations of the above! Questions?
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