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.

From attributes 8 3 Implementation 8 3.1 Overview of Meta-Rulegen 3.2 Algorithm 10 3.2.1 Approach from object rule 11 3.2.2 Approach from attributes 14 4 Preliminary Results 15 5 Conclusion 17 ENHANCING PERFORMANCE OF EXPERT SYSTEMS BY AUTOMATED DISCOVERY OF META-RULES Abstract Machine learning can be used to formulate new meta-level knowledge. A small MYCIN-like medical diagnosis system was constructed as a starting point. Two heuristic methods are used in a program called Meta-Rulegen to form meta-rules from the knowledge base in the diagnosis system. In a preliminary study, 63 meta-rules were formed automatically and, by judiciously selecting a set of meta-rules, the efficiency of the diagnosis system can be improved significantly without degrading the quality of advice. This study suggests that meta-rules can be learned automatically to improve the efficiency of rule-based systems.

Although informal models of evidential reasoning have been successfully app'ied in automated reasoning systems, it is generally difficult to define the range of their applicability In addition, they hay., not provided a basis for coherent management of evidence bearing on hypotheses that are related hierarchically. The Dempster-Shafer (D-S) theory of evidence is appealing because it does suggest a coherent approach for dealing with such relationships However, the theory's complexity and potential for computational inefficiency have tended to discourage its use in reasoning systems In this paper we describe the central elements of the D-S theory, basing our exposition on simple examples drawn from the field of medicine. We then demonstrate the relevance of the 0-S theory to a familiar expert system domain, namely the bacterial organism identification problem that lies at the heart of the MYCIN system. Finally, we present a new adaptation of the D-S approach that achieves computational efficiency while permitting the management of evidential reasoning.within

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.

Researchers in the development of medical expert systems have Increasingly recognized the Importance of explanation capabilities in encouraging the acceptance of their programs. One survey of potential users of medical advice systems has suggested that explanation may be the single most important capability of an acceptable clinical decision tool (16). Good explanations serve four functions in a consultation system: 111 they provide a method for examining the program's reasoning if errors arise when the system is being built; 121 they assure users that the reasoning is logical, thereby increasing user acceptance of the system; 131 they may persuade users that unexpected advice is appropriate; and 141 they can educate users in areas where their knowledge may be weak.

The ONCOCIN Project is supported by research grants from the National Library of Medicine, the Division of Research Resources of the NIH, the Office of Naval Re"arch, and the Henry J. Kaiser Family Foundation.

Reprinted by permission of the author. Published in the Proceedings of a Symposium on Computers in Medicine, Annual Meeting, California Medical Association, Anaheim, CA., February 1984. Alt;iough computing technology is playing an increasingly important role in medicine, systems designed to advise physicians on diagnosis or therapy selection have remained largely experimental to date. Despite diverse research efforts, and a literature on computer-aided diagnosis that has numbered over 1500 references in the last 20 years, clinical consultation programs have failed to achieve wide acceptance. The reasons for attempting to develop such systems are self-evident.