On August 18 and 19, 1980, at Stanford U n i v e r s i t y during at Stanford U n i v e r s i t y, with the actual game on i n a c l o s e d the A A A I conference, the f i r s t of a projected p a i r of annual room containing o n l y the player, computer terminal operators c h e s s competitions pitting the w o r l d ' s best computer programs (L a r r y A t k i n and David Cahlander of Control Data Corporation) against rated human p l a y e r s of approximately the same and the referee. U p s t a i r s was a large demonstration strength was held. These matches are part of the F r e d k i n p r i z e room where two boards, one for the actual position and one competition, wherein a sum of $100,000, established by the for a n a l y s i s, were used to keep the audience abreast of what F r e d k i n Foundation of Cambridge, Mass, i s to be awarded to was happening and could be expected to happen. The moves the creators of a program that can defeat the W o r l d Chess were communicated through a telecommunications setup Champion i n o f f i c i a l competition. The program i n t h i s match was C H E S S 4.9 of Northwestern In the f i r s t game, C H E S S 4.9 played the White s i d e of a U n i v e r s i t y, authored by David S l a t e and L a r r y A t k i n.

The Yale Artificial Intelligence Project, under the direction of Professor Roger C. Schank, supports a number of research projects. Most of this research is in the02-02 area of attempting to model the processes involved in human understanding, with a current emphasis on memory models and the processes involved in learning.

By-Laws of the Association for the Advancement of Artificial Intelligence

AI research at CMU is closely integrated with other activities in the Computer Science Department, and to a major degree with ongoing research in the Psychology Department. Although there are over 50 faculty, staff and graduate students involved in various aspects of AI research, there is no administratively (or physically) separate AI laboratory. To underscore the interdisciplinary nature of our AI research, a significant fraction of the projects listed below are joint ventures between computer science and psychology.

This article is the second planned excerpt from the Handbook of Artificial Intelligence being complied at Stanford University. This overview of the Handbook chapter on search, like the overview of natural language research we printed in the first issue, introduces the important ideas and techniques, which are discussed in detail later in the chapter. Cross-references to other articles in the Handbook have been removed -- terms discussed in more detail elsewhere are italicized. The author would like to note that this article draws on material generously made available by Nils Nilsson for use in the Handbook.

Twenty five years is not long in the history of a science--long enough to achieve, short enough to remember. Your esteemed founders are still around -- vigorous, not so young anymore. Out of the cybernetics you came, and information-theoretic psychology. You were born in the early days of modern computing, on hot, bulky hardware with names few now remember, like JOHNNIAC; in strange and wonderful software called list structures, with stacks you could "push down" and "pop-up," bearing arcane acronyms like IPL and FLPL.

The Robotics Project (the "Hand-Eye Project") evolved within the Stanford Artificial Intelligence Laboratory under the guidance of John McCarthy, Les Earnest, Jerry Feldman, and Tom Binford. Major efforts have been undertaken to isolate and solve fundamental problems in computer vision, manipulation, and autonomous vehicles. Stereo vision and texture have been examined. Several generations of robot programming languages have resulted in AL, an intermediate-level language for commanding manipulation.

For intelligent computers to be able to interact with the real world, they must be able to aggregate individual actions into sequences to achieve desired goals. This process is referred to as automatic problem solving, sometimes more casually called automatic planning. The sequences of actions that are generated are called plans.

This overview takes a general look at search in problem solving, indicating some connections with topics considered in other Handbook chapters. The these general ideas are found in programs for natural second section considers algorithms that use these language understanding, information retrieval, automatic representations. In methods, which use information about the nature and this chapter of the Handbook we examine search as a tool structure of the problem domain to limit the search. Most of the Finally, the chapter reviews several well-known early examples considered are problems that are relatively easy programs based on search, together with some related to formalize. The first of these is a may be, however, that the description of a task-domain database, which describes both the current task-domain situation is too large for multiple versions to be stored situation and the goal.

AI research at CMU is closely integrated with other activities in the Computer Science Department, and to a major degree with ongoing research in the Psychology Department. Although there are over 50 faculty, staff and graduate students involved in various aspects of AI research, there is no administratively (or physically) separate AI laboratory. To underscore the interdisciplinary nature of our AI research, a significant fraction of the projects listed below are joint ventures between computer science and psychology.