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.

"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.

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.

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.