Introduction to Artificial Intelligence Ruth Bergman Fall 2004

What is an Artificial Intelligence? Genghis EduSpeak ALVINN See-Threepio Kismet

Definitions of AI “[The automation of] activities that we associate with human thinking…” [Bellman 1978] “The study of how to make computers do things at which, at the moment, people are better [Rich and Knight, 1991] “The study of mental faculties through the use of computational models” [Charniak and McDermott, 1985] “The branch of computer science that is concerned with the automation of intelligent behavior” [Luger and Stubblefield, 1993] Note: - think vs. act - human vs. rational

Cognitive Modelling Construct programs that think like humans. How do humans think? Introspection or psychological experiments. Why imitate human thought? –To solve problems. –To learn about human cognitive processes. Cognitive Science: the interdisciplinary field that brings together AI and psychological experimental techniques to try to understand workings of the human mind.

Turing Test How can we evaluate intelligence? Turing [1950] a machine can be deemed intelligent when its responses to interrogation by a human are indistinguishable from those of a human being. interrogator human machine

Turing Test Requires solving hard problems: –natural language processing –knowledge representation –automated reasoning –machine learning Is the test valid? –Too strict: is human intelligence the only form of intelligence –Too lax: is giving the appearance of intelligence sufficient

Rational Thought: Logic The logicist school of thought in AI uses formal logic techniques to automatically solve problems. Successes in fields that lend themselves to formal description, such as mathematics. Obstacles to this approach: –Real-world problems are difficult to formalize –Computation time proves a barrier for realistic problems in practice.

The rational agent The agent is assumed to exist in an environment in which it perceives and acts The agent is rational if it acts to attain its goals given its belief about the environment Motivation: –Not all thought is rational, e.g. reflex –A correct action may not exist using inference –Addresses the cognitive skills required by the Turing Test –Avoids the complex issue of behaving humanly which is impossible to measure.

A Brief History of AI 1960’s: basic techniques, microworlds Logic, Lisp, Games (checkers) 1970’s: Knowledge-based systems Expert systems (DENDRAL, MYCIN) 1980’s: AI in industry, machine learning. Industrial expert systems, Lisp Machine, industrial vision 1990’s: Rational agent, multi-agent systems, formal methods Decision making, whole agent approach

AI Fields of Study Core AI: –Problem solving games –Knowledge representation medical diagnosis –Reasoning theorem proving –Learning neural networks Vision face recognition Natural Language speech recognition Robotics mobile robots

The Intelligent Agent Anything that perceives its environment through sensors and acts on its environment through effectors. performance measure: goals autonomy Polly, a vision-based artificial agent (1994) sensor effector

Structure of Intelligent Agents Agent Environemnt Sensors Effectors agent program

Agent Research Agent typePerceptsActionsGoalsEnvironment characteristics Mobile robotObstacle ahead Move ahead, turn Do not collideDynamic, continuous, inaccessible social humanoid robot Pixels of varying intensity, color, speech Display happy, sad, angry… face Engage humans Dynamic, continuous, accessible Personal assistant textSort, inform user Correct categorization Dynamic, discrete, accessible

The agent program The agent program maps from percepts to actions The agent uses knowledge representation to model the environment The agent must reason with knowledge and make decisions The agent may learn to improve its world model and decision making ability. Thus all AI techniques are part of the intelligent agent.

Components of the agent program Agent Environemnt Sensors Effectors Representation of the world Inference & Action selection goals World model

Components of the Polly program Sensors: b/w video camera Effectors: wheels, voice synthesizer Representation of the world: location in map, tour group Agent Environemnt Sensors Effectors Rep Act? goals World model World Model: map of MIT AI Lab Goals: conduct tour Decision making: avoid obstacles, wander, follow corridor, tour