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).

The search is "small" in the sense that the size of

We learn (memorize) multiplication tables, learn (discover how) to walk, learn (build UP an understanding of, then an ability to synthesize) languages. Many subtasks and capabilities are involved in these various kinds of learning. One capability central to many kinds of learning is the ability to generalize: to take into account a large number of specific observations, then to extract and retain the important common features that characterize classes of these observations. This generalization problem has received considerable attention for two decades in the fields of Artificial Intelligence, Psychology, and Pattern Recognition (e.g., [Bruner, 1956],

The next step is to find an algorithm for finding paths in P2, then apply this al!drithin in a certain way as a heuristic Many combinatorially large problems cannot be solved for P1. As an elementary example, the rectilinear distance feasibly by exhaustive case analysis or brute force function is an efficient heuristic for finding paths in a search, but can be solved efficiently if a heuristic can be "Manhattan street pattern" graph even when some (but devised to guide the search. Finding such a heuristic for not too many) of the streets have been blockaded (i.e., a given problem, however, usually requires an exercise of some edges are removed from the. graph).

"Almost all the basic methods used by

In this paper we present a new algorithm for searching trees. The algorithm, which we have named For this reason, the search is usually limited in some way (e.g., number of nodes B*, finds a proof that an arc at the root of a search tree is better than any other. It does this by to be expanded, or maximum depth to which it may go).

Marvin Lee Minsky was born in New York on 9th August, 1927. He received his B.A from Harvard in 1950 and Ph.D in Mathematics from Princeton in 1954. For the next three years he was a member of the Harvard University Society of Fellows, and in 1957-58 was staff member of the M.I.T. Lincoln Laboratories. At present he is Assistant Professor of Mathematics at M.I.T. where he is giving a course in Automata and Artificial Intelligence and is also staff member of the Research Laboratory of Electronics. SUMMARY THIS paper is an attempt to discuss and partially organize a number of ideas concerning the design or programming of machines to work on problems for which the designer does not have, in advance, practical methods of solution. Particular attention is given to processes involving pattern recognition, learning, planning ahead, and the use of analogies or?models!. Also considered is the question of designing "administrative" procedures to manage the use of these other devices.

If ability to perform complex calculations were a sufficient criterion, then even a conventional digital computor could lay claim to more intelligence than any of usand perhaps we had better let it make away with the word and be done with it.

In this paper, we consider a distinctly bottom-up which generates arguments by performing a heuristic approach.

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE