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.

Specifically, it is concerned with control strategies governing the formation and refinement of partial hypotheses about the identity of an utterance that can guarantee the discovery of the best possible interpretation. We assume a system that contains the following components: a) A Lexical Retrieval component that can find the k best matching words in any region of an utterance subject to certain constraints and can be recalled to continue enumerating word matches in decreasing order of goodness (where possible constraints include anchoring the left or right end of the word to particular points in the utterance or to particular adjacent word matches).

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

The Hearsay-H speech-understanding system (SUS) developed at Carnegie-Mellon University recognizes connected speech in a 1000-word vocabulary with correct interpretations for 90 percent of test sentences. Its basic methodology involves the application of symbolic reasoning as an aid to signal processing. A marriage of general artificial intelligence techniques with specific acoustic and linguistic knowledge was needed to accomplish satisfactory speech-This research was supported chiefly by Defense Advanced Research Projects Agency contract F44620-73- C-0074 to Carnegie-Mellon University. In addition, support for the preparation of this paper was provided by USC/ISI, Rand, and the University of Massachusetts. We gratefully acknowledge their support. Views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official opinion or policy of DARPA, the U.S. government, or any other person or agency connected with them.

The purpose of this research is to investigate the extent to which knowledge can replace and support search in selecting a chess move and to delineate the issues involved. This has been carried out by constructing a program. PARADISE (PArtern Recognition Applied to Directing SEarch), which finds the best move in tactically sharp middle game positions from the games of chess masters.

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

PATHOLOGY ON GAME TREES: A SUMMARY OF RESULTS* Dana S. Nau Department of Computer Science University of Maryland College Park, MD 20742 ABSTRACT Game trees are widely used as models of various decision-making situations. Empirical results with game-playing computer programs have led to the general belief that searching deeper on a game tree improves the quality of a decision. The surprising result of the research summarized in this paper is that there is an infinite class of game trees for which increasing the search depth does not improve the decision quality, but instead makes the decision more and more random. This research has produced the surprising result that there is an infinite class of game trees for which as long as the search does not reach the end of the tree (in which case the best possible decision could be guaranteed), deeper search does not improve the decision quality, but instead makes the decision more and more random. For example, probability of I INTRODUCTION - Many decision-making processes are naturally modeled as perfect information games between two players [3, 71.

An algorithm based on state space search is introduced for computing the minimax value of game trees. The new algorithm SSS∗ is shown to be more efficient than α-ß in the sense that SSS∗ never evaluates a node that α-ß can ignore. Moreover, for practical distributions of tip node values, SSS∗ can expect to do strictly better than α-ß in terms of average number of nodes explored. In order to be more informed than α-ß, SSS∗ sinks paths in parallel across the full breadth of the game tree. The penalty for maintaining these alternate search paths is a large increase in storage requirement relative to α-ß.