Artificial Intelligence Overview - Montana State University

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

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). 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. 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




MMM C Types of Search Blind 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 M C 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. Minimax Example Nim 4 3 2

1 1 (my move) 2 1 1

(your move) 1 (my move) (your move) Chess Example maximizer

* * 3 * 0 -5

minimizer * 4 10 2 Knowledge Representation Semantic Nets

Fuzzy Logic First Order Predicate Calculus Semantic Nets can-fly yes bird is-a

is-a is-a robin magpie no ostrich can-fly Fuzzy Logic

Shaquille ONeal is tall 1.0 tall 0.0 50 60 70 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 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

sensors actuators AGENT ENVIRONMENT Rationality Factors Performance Measure Prior Knowledge

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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