Introduction to Artificial Intelligence 236501 Ruth Bergman Fall

Introduction to Artificial Intelligence 236501 Ruth Bergman Fall 2002

Kismet What is an Artificial Intelligence? Genghis Edu. Speak ALVINN See-Threepio

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 Mc. Dermott, 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 • Natural Language • Robotics face recognition speech recognition mobile robots

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

Structure of Intelligent Agents Sensors Agent Effectors Environemnt agent program

Agent Research Agent type Percepts Actions Goals Environment characteristics Mobile robot Obstacle ahead Move ahead, turn Do not collide Dynamic, continuous, inaccessible social humanoid robot Pixels of varying intensity, color, speech Display happy, Engage sad, angry… humans face Dynamic, continuous, accessible Personal assistant text Sort, inform user Dynamic, discrete, accessible Correct categorization

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

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