A new method for automated planning, progressive refinement of skeletal plans, has been developed for the problem of experiment design in the domain of molecular biology. The method resulted from a study of the problem-solving behavior of scientists which showed that design usually consisted of lookup of abstracted plans followe6 by hierarchical plan-step refinement. The skeletal plan method has been implemented through two generations of problem-solving systems: the second generation involving a synthesis with the metaplanning approach of Stefik.

This article presents an experiment in knowledge-intensive programming within a general problemsolv:ng production-system architecture called Soar In Soar, knowledge is encoded within a set of problem spaces.

We have developed an automated system for the design of laboratory experiments in molecular biology. The system uses a planning method known as skeletal plan refinement that attempts to emulate the human cognitive task of experiment design. This paper describes the theory, history, and implementation of the design system and illustrates its function in the domain of DNA cloning experiments.

Stanford -- KSL that Think. Consideration of the of the new 16-bit integrated circuits that phenomenal progress of the past 30 years leaves one with a feeling of have allowed computers oi small size and considerable power to be developed. The only certainty in sight is that scientists. BRUCE G. BUCHANAN is Professor of In addition to game playing early Al work focused on techniques for solving Computer Science Research at Stanford small symbolic reasoning problems. Researchers continue to ponder these problems (Overleat) Illustration by f red Nelson as well.

This means that we by watching the expert diagnose a patient. Our approach relies heavily on a close correspondence are trying to create a framework whereby an between the system and a human expert problem solver's knowledge organization with respect to knowledge organization, inference and knowledge acquisition methods are modeled methods and discourse language. The described system is a major component of a learning as similarly as possible to human problem by watching system being created to facilitate solvers.

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

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.

Current subprojects include applications of the program Gerring.

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.