This paper reports the results obtained with a group of 24 14-year-old pupils when presented with sets of algebra tasks by the Leeds Modelling System.

The objective of this paper is to present a new method for the computer representation of empirically derived causal relationships (CR's). This method draws on the theory of multivariate linear models and path analysis. The method is contrasted with the predicate calculus methods developed by other Al researchers. The representation presented here has been used to store information on medical CR's derived empirically from a large clinical database by a computer program called RX. The principal emphasis in the representation is on capturing the intensities and variances of effects and the variation in the effects across a patient population. Once incorporated into RX's knowledge base, this information is subsequently used by RX in determining the validity of other CR's. The representation uses a directed graph formalism in which the nodes are frames and the arcs contain seven descriptive features of individual CR's: intensity, distribution, direction, mathematical form, setting, validity, and evidence. Because natural systems (such as the human body) are inherently probabilistic, linear models are useful in representing causal flow in them. Knowledge of natural systems is fundamentally probabilistic because of I) irreducible indeterminism in their component processes, 2) difficulties in accurately measuring all relevant variables, 3) variation among individuals in a population, and 4) inadequate scientific theory.

The time-sharing systems that were used to develop them are largely inadequate due to the burden placed upon computing and storage resources. Consequently, some of this research is being shifted to personal workstations which provide a sophisticated graphics interface in addition to satisfying the requirements for computational speed and memory. This paper examines the use of that graphics interface in the development of a tool for a system builder. Introduction Medical expert systems are consultation programs designed -..o give advice using both formal knowledge and the judgmental expertise of clinical specialists. When these systems first began to appear in the 1970's, many observers doubted their future role because their size and complexity placed an inordinate burdenlin - the processing and storage resources of conventional time-sharing systems.

People's certainty of the past is D follows from A. B. and C. It may be that A. B. and C, limited by the fidelity of the devices that record it, their though certain, suggest but do not confirm D. in which case knowledge of the present is always incomplete, and their the number associated with D might be less than the 1.0 that knowledge of the future is but speculation. Even though usually represents certainty in such systems. If A. B. or C nothing is certain, people behave as if almost nothing is are uncertain, then the number associated with D is modified uncertain. They are adept at discounting uncertainty -- to account for the uncertainty of its premises. These numbers making it go away. This article discusses how Al programs are given different names by different authors; we refer might be made similarly adept.

FOLIO is an expert system to assist portfolio managers. It interviews a client and, on the basis of expert knowledge, determines the client's investment goals and the portfolio that best meets them. For example, FOLIO may determine that one client requires a preponderance of tax-free investments and a substantial hedge against rising short-term interest rates, while another is best served by a mix of low-risk dividend-oriented stocks and intermediate-term bonds. FOLIO is a test bed for a theory of heuristic reasoning about uncertainty (Cohen and Grinberg, 1983), and its task has many parallels to estab!ished Al paradigms such as diagiosis in medicine and construction of a student model in ICAI domains (Barr and Feigenbaum, 1982). FOLIO uses a goal programming algorithm (Hillier and Lieberman, 190) as a relaxation method for resolving the client's multiple goals into a portfolio that fi'.s them optimally.

To appear inProc. of the American Association for Medical Systems & Informatics, 1983 Reprinted by permission of the American Association for Medical Systems and Informatics (AAMSI). Hardware advances in the next decade promise to make poss:*ale new medical educational technologies. New media for expressing, collecting, and sharing knowledge will provide students with means for coping with the increasing amounts of information. Novel means of graphically modelling physical phenomena--providing motivating and intuitively pleasing means for explorative interaction--could complement and sometimes replace traditional text material. Intelligent programs may serve as assistants, serving roles ranging from calculator to librarian to tutor, embracing a full range of secretarial and problen solving aids.

It has been argued that the problem of medical diagnos.r_s is fundamentally ill-structured.

John Doe - 12-34-56 Lhe patient should receive chemotherapy (PAVE-3A).

Interactive intelligent systems often suffer from a basic conflict between their computationally intensive nature and the need for responsiveness end-user acceptability.

Report 82 38 The Computer and Medical Decision Making: Stanford - KSL Good Advice is Not Enough. Reprinted, with permission, from Engineering in Medicine and Biology Magazine 1,1992. Mailing address: Medical Computer Science, Room TC-117, Division of General Internal Medicine, Stanford University School of Medicine, Stanford, California 94305. Dr. Shortliffe is recipient of Research Career Development Award LM-00048 from the National Library of Medicine. Much of the training of physicians is designed to facilitate optimal, informed clinical decision making.