"Coming to Terms With the Computer" by Edward H. Shortliffe reprinted with permission from The Machine at the Bedside, Eds. You are asked to assist a major teaching hospital in the assessment of a large computer system that was installed 3 months ago to help with doctors' orders, laboratory test reporting, nursing schedules, and bed control. Because of mixed reviews of the system's effectiveness, the hospital has decided to bring in outside experts to assess the computer's strengths and weaknesses. The computer system was installed by a vendor of large-scale hospital information systems (HIS). The company had developed the programs over several years, but this is its first major commercial installation.

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

COMPUTER SCIENCE DEPARTMENT Stanford University Stanford, California 94305 Commu licaticn and Cooperation Abstract Intelligent agents need to coordinate their actions in pursuit of common goals. When communication is possible, cooperating agents must decide what information to pass in order to agree on a single course of action. This paper outlines several communication strategies (under monotonic and nonmonotonic planning assumptions), proving that some are convergent while others are not. An analysis is also made of the advantages of passing false information. Introduction Recent years have seen increasing interest in Distributed Artificial Intelligence (DAI) systems, that is, in groups of intelligent agents whose members cooperate in carrying out tasks. Considerable work has gone on in this area, producing a number of tentative approaches to cooperation; notable among these research efforts are Smith and Davis' work on the Contract Net [1], Davis' investigations of Cooperative Problem Solving strategies [2], Georgeff's approach to assuring non-interference among distinct agents' plans [3, 41, and Lesser and Corkill's empirical analyses of distributed computation 151. Despite some genuine insights that these researchers have gained, however, DAI has lacked much of the formal foundation needed for progress. Recent work by Appelt 161, Moore [7, 8] and Icon lige 19, 10, 11, 121 has begun to develop the formal descriptions necessary for one agent to reason about another agent's knowledge and beliefs; this is a key step in the development of successful DAI systems. This paper begins to lay the groundwork for another aspect of Distributed Artificial Intelligence's foundation; it presents a description and analysis of information pass:ng strategies between intelligent agents. Through use of a formal descriptive language, certain information passing behavior is proven to be convergent. In addition, an analysis is made of the role that can be played by the passing of false information, i.e., information that is logically inconsistent with the beliefs of the sender. Consider, for example, two individuals who have lost contact with each other in a department store [131.

However, many 3pecialized Many languages have the property that when languages are limited in their expressive power. This some collections of facts are stated explicitly, additional paper presents methods for determining when a set of facts are stated implicitly. We call such languages facts is expressible in a language.

A complete program is one that for any environment specifies a unique action for a machine to perform. Programs in most traditional programming languages are complete in this sense. By contrast, a partial program is merely an arbitrary set of constraints on the potential actions of a machine and does not necessarily specify a unique action in every enviranment.

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.