These links are based on domain facts and relations (e g. that a topic is the name of a block of Step?: Discuss the final hypothesis for the goal

A clinical investigator graphical capabilities which can plot specific parameters for a keeping the records of his study patients on such a system can patient over time 1126]. However, it is in the analysis of stored use the program's statistical capabilities for data analysis.

These knowledge bases are generally built by interviewing human experts to extract the knowledge they use to solve problems in their area of expertise. However, it is not clear that the organization and level of abstraction of this performance knowledge is suitable for use in a tutorial program. We are exploring this problem in the GUIDON tutorial program, using the knowledge bases of MYCIN-like expert systems. MYCIN is a knowledge-based program that provides consultations about infectious disease diagnosis and therapy (Shortliffe, 1974). In MN CIN, domain relations and facts take the form of rules about what to do in a given circumstance. A principle feature of this formalism is the separation of the knowledge base from the interpreter for applying it. This makes the knowledge accessible for multiple uses, including application to particular problems (i.e. for "performance") and explanation of reasoning (Davis, 1976). We have most recently used the MYC1N knowledge base as the foundation of a tutorial system, called GUIDON.

Because of the recent - echnological advances in computer networks and communications, and because of the cost reduction of computer hardware, there has been a great interest in distributed data bases including some attempts at actual implementations. In this paper, we will first define what we mean by a distributed data base. Then we will give some of the reasons why people are so interested in this new field. After classifying the different types of distributed data bases, we will describe the current areas of research. Finally, we will give an annotated bibliography that lists the most important papers in thi:3 area.

Our understanding of any process can be measured by the extent to which a simulation we create mimics the real behavior of that process. Deviations of a simulation indicate either limitations or errors in our knowledge. In addition, these observed differences often suggest verifiable experimental hypotheses to extend our knowledge. The biochemical approach to understanding biological processes is essentially one of simulation. A biochemist typically prepares a cell-free extract that can mediate a well-described physiological process. The extract is then fractionated to purify the components that catalyze individual reactions.

Human scientists actively seek out information that bears on questions they have decided to pursue. They design experiments, explore the implications of the knowledge they have, refine their questions and test alternative ideas. Although many discoveries are the result of unexpected observations, these surprises take place in the context of an explicit pursuit of knowledge. Viewing scientific discovery as a kind of motivated action raises some basic issues common to goal-directed behavior generally: Where do desires (to know) come from? What are the actions that can be taken (to discover)? What are the resources those actions consume, and how are they allocated? How are decisions about selecting and combining actions made?

Methods of Approach At the outset it might be well to distinguish sharply between two general approaches to the problem of machine learning.

The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

Case-based reasoning is used extensively by people in A second driving force in the evolutionary history of CBR both expert and commonsense situations. It provides a was dissatisfaction with rule-based reasoning (expert systems wide range of advantages.

Empirical rule learning and analytic Most learning is based on experience, and this requires a learning methods have predominantly used the first path, representation for the experiential input given to the whereas connectionist systems have relied on the second.