"Circumscription is a rule of conjecture that can be used by a person or program for `jumping to certain conclusions'. Namely, the objects that can be shown to have a certain property P by reasoning from certain facts A are all the objects that satisfy P. More generally, circumscription can be used to conjecture that the tuples that can be shown to satisfy a relation P(x, y, z) are all the tuples satisfying this relation. Thus we circumscribe the set of relevant tuples."Artificial Intelligence 13:27-39. Also in Readings in Artificial Intelligence, B.L. Webber and N.J. Nilsson (eds.), Tioga Publishing, 1981.

After presenting the design considerations and their relationship to AI research, I will use our work with MYCIN to illustrate some of the ways in which we have attempted to respond to the acceptability criteria I have outlined.

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.

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.

The spatial and qualitative aspects of reasoning about motion through fret space arc studied through the construction of a program to perform such reasoning. An analog gcomctry rcprcscntation serves as a diagram, and descriptions of both the actual motion of a ball and envisioning are used in answering simple questions. WC ignore the exact shape of balls, motion after two balls collide, spin, motion in a third spatial dimension, air resistance, sliding motion, and all forces other than gravity. The initial description of a situation is a diagram containing a description of the surfaces and one or more bails, as in ligure 1. 1 Introduction People reason fluently about motion through space. For example, WC know that if two balls arc thrown into a well they might collide, but if one ball is always outside and the other always inside they cannot. The knowl"cdgc involved in this qualitative kind of reasoning seems to be simpler than formal mechanics and appears to be based on our expcricnce in the physical world.

Truth maintenance systems have been used in several recent problem solving systems to record justifications for deduced assertions, to track down the assumptions which underlie contradictions when they arise, and to incrementally modify assertional data structures when assumptions are retracted. A TMS algorithm is described here that is substantially different from previous systems. This algorithm performs deduction in traditional propositional logic in such a way that the premise set from which deduction is being done can be easily manipulated. A novel approach is also taken to the role of a TMS in larger deductive systems. In this approach the TMS performs all propositional deduction in a uniform manner while the larger system is responsible for controlling the instantiation of universally quantified formulae and axiom schemas.

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This report describes a knowledge-base system in which the information is stored in a network of small parallel processing elements??de and link units??ich are controlled by an external serial computer. This network is similar to the semantic network system of Quillian, but is much more tightly controlled. Such a network can perform certain critical deductions and searches very quickly; it avoids many of the problems of current systems, which must use complex heuristics to limit and guided their searches. It is argued (with examples) that the key operation in a knowledge-base system is the intersection of large explicit and semi-explicit sets. The parallel network system does this in a small, essentially constant number of cycles; a serial machine takes time proportional to the size of the sets, except in special cases.

Frederick Hayes-Roth The RAND Corporation Using the concepts of stimulus and response frames of scheduled Knowledge source instantiations, competition among alternative responses, goals, and the desirability of a knowledge source instantiation, a general attentional control mechanism is developed. This general focusing mechanism facilitates the experimental evaluation of a variety of specific attentional control policies (such as best-first, bottom-up, and top-down search strategies) and allows the modular addition of specialized heuristics for the speech understanding task. Empirical results demonstrate the effectiveness of the focusing principles, and possible directions for future research are considered. INTRODUCTION The Hearsay-II (HSII) speech understanding system (Lesser, et al., 1974; Erman & Lesser, 1975; Lesser & Frman, 1977) is a complex, distributed-logic processing system. Inputs to the system are temporal sequences of sets of acoustic segments and associated hypothesized phonetic labels.

To choose their actions, reasoning programs must be able to make assumptions and subsequently revise their beliefs when discoveries contradict these assumptions. The Truth Maintenance System (TMS) is a problem solver subsystem for performing these functions by recording and maintaining the reasons for program beliefs. Such recorded reasons are useful in constructing explanations of program actions and in guiding the course of action of a problem solver. This paper describes (1) the representations and structure of the TMS, (2) the mechanisms used to revise the current set of beliefs, (3) how dependency-directed backtracking changes the current set of assumptions, (4) techniques for summarizing explanations of beliefs, (5) how to organize problem solvers into "dialectically arguing" modules, (6) how to revise models of the belief systems of others, and (7) methods for embedding control structures in patterns of assumptions. We stress the need of problem solvers to choose between alternative systems of beliefs, and outline a mechanism by which a problem solver can employ rules guiding choices of what to believe, what to want, and what to do.Artificial Intelligence 12(3):231-272