1 / 16

Introduction to Artificial Intelligence 236501

Introduction to Artificial Intelligence 236501. Ruth Bergman Fall 2002. What is an Artificial Intelligence?. Kismet. Genghis. EduSpeak. See-Threepio. ALVINN. Definitions of AI. “[The automation of] activities that we associate with human thinking…” [Bellman 1978]

willieestrada
Download Presentation

Introduction to Artificial Intelligence 236501

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction to Artificial Intelligence236501 Ruth Bergman Fall 2002

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

  3. 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

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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)

  12. Structure of Intelligent Agents Sensors agent program Environemnt Agent Effectors

  13. Agent Research

  14. 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.

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

  16. Sensors World model Rep Environemnt Act? goals Effectors Agent 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

More Related