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.

Finally, abstraction can be extended to involve multiple complexity. In particular, one of the most costly behaviors levels, leading to a hierarchy of plans, each serving as a of the basic problem solving strategies is their inefficiency skeleton for the problem solving process at the next level in dealing with goal descriptions that include conjunctions. of detail. The search process at each level of detail can Because there is usually no good reason for the problem thus be reduced to a sequence of relatively simple solver to prefer to attack one conjunct before another, an subproblems of achieving the preconditions of the next incorrect ordering will often be chosen. This can lead to step in the skeleton plan from an initial state in which the an extensive search for a sequence of actions to try to previous step in the skeleton plan has just been achieved.

Artificial Intelligence

ACM Computing Survey 12:213-253

Earlier version in IJCAI 6 (1979). Artificial Intelligence, 14 (2), 165-203.

In Clarke, M. R. B. (Ed.), Advances in Computer Chess 2, pp. 103-109. Edinburgh University Press

In AAAI-80, pp. 102–104.

In Clarke, M. R. B. (Ed.), Advances in Computer Chess 2, pp. 103-109. Edinburgh University Press

Artificial Intelligence, 12 (1), 23-40.