Symbolic inference since the time of Aristotle has involved the combination of symbolic expressions.

Multiple processors can be used to achieve a speedup of a backward-chaining deduction by distributing or-parallel deductions. However, the actual speedup obtained is strongly dependent on the task allocation strategy. Also, communication cost can be a significant part of the overall cost of a deduction. For the multiple processor scenario used in this paper,, processors with replicated databases on a broadcast network, a variable supply model (VSM) is presented. VSM represents an infinite class of strategies with varying communication requirements.

A broad range of heuristic programs--embracing forms of diagnosis, catalog selection, and skeletal planning--accomplish a kind of well-structured problem solving called classification. These programs have a characteristic inference structure that systematically relates data to a preenumerated set of solutions by abstraction.

Loosely speaking, recursive inference is when an inference procedure generates an infinite sequence of similar subgoals. In general, the control of recursive inference involves demonstrating that recursive portions of a search space will not contribute any new answers to the problem beyond a certain level. We first review a well known syntactic method for controlling repeating inference (inference where the conjuncts processed are instances of their ruicestors), provide a proof that it is correct, and discuss the con- (Mims under which the strategy is optimal. We also derive more powerful pruning theorems for rases involving transitivity axioms arid cases involving subsumed subgoals. The treatment of repeating inference is followed by consideration of the More difficult prr)liIon of recursive inference Crat does not repeat. Here we show bow knowledge of the properties of the relations involved and knowledge about the contents of the system's database can be used to prove that portions of a search space will not contribute any new .az

Millions of base pairs worth of DNA sequences are now known and must be analyzed: hundreds of restriction enzymes and cloning vectors must be kept track of to carry out work in genetic engineering. Computational systems have become a necessary tool to acquire, retrieve, manipulate, and analyze this information. A myth of the early days of computing was that computers were excellent number manipulators, but poor at working with other forms of information.

Current subprojects include applications of the program Gerring.

Although computer-based instruction has become widely available as a learning aid in medical education, few physicians interact with educational programs after they have left medical school. Some notable exceptions occur when specially prepared computer programs are made available by vendors or program committees at annual clinical meetings. Yet this kind of learning tool is seldom used by practicing physicians at other times during the year. In this paper, I would like to consider ways in which computer-based education might be more effectively integrated into the clinical activities of the practicing physician, and to outline some of the technological and psychological barriers to their successful implementation.

J. J. Finger and Michael R. Genesereth Computer Science Department Stanford University, Stanford, California 94305 1. Introduction Automatic theorem proving methods, such as resolution or backwards-chaining, arc useful for verifying the correctness of a pre-cxisting design, but how might we use theorem proving methods to both generate and verify a design?

Reprinted, with permission, from IEEE Acoustic, Speech and Signal Processing, Spring, 1984. ABSTRACT In the past fifteen years, artificial intelligence scientists have built several signal interpretation, or understanding, programs. These programs have combined "low" level signal processing algorithms with knowledge representation and reasoning techniques used in knowledge-based. HASP/SIAP is one such program that tries to interpret the meaning of passively collected sonar data. In this paper we explore some of the Al techniques that contribute in the "understanding" process. We also describe the organization of HASP/SIAP system as an example of a programming framework that show promise for applications in a class of similar problems.1 Using data from concealed hydrophone arrays, it must detect, localize, and ascertain the type of each ocean vessel within range. Tne presence and movements of submarines are of most interest, but there are strategic and tactical motives for monitoring all vessel types.

MARS is an experimental system that provides a framework for implementing hierarchical discrete event driven simulators. The program is independent of any domain.