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

In Al knowledge representation schemes, structures that describe other structure:: are said to represent "meta-knowledge." Knowledge about other knowledge can be either about the form of the representation scheme itself (e.g., its syntax) or about the "facts" that are represented (their origin, reliability, Importance, etc.). After reviewing the use of explicit meta-knowledge In several systems, some studies of human behavior that Indicate people's ability to reason about what they know and about how they reason are described. The concept of meta-level knowledge captures intrinsic, commonplace properties of human cognition that are central to an understanding of memory and Intelligence. The use of meta-knowledge In Al systems like MYCIN, which have reached humanexpert-level performance In complex domains, Is a key breakthrough In the design of "knowledge-based" Intelligent systems. Meta-level knowledge has been used in these systems primarily in the implementation of "introspective" processes: Acquisition of new knowledge and explanation of the system's reasoning to users. The usefulness of meta-level descriptions for these and other functions has prompted proposals for their incorporation in several new general-purpose representation schemes, like KRL, as described In the next section.

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.

In the early days of computing, these goals were central to the new discipline called cybernetics [126], [2]. Over the past two decades, progress toward these goals has come from a variety of fields - notably computer science, psychology, adaptive control theory, pattern recognition, and philosophy. Substantial progress has been made in developing techniques for machine learning in highly restricted environments.

An opposing school of thought, sometimes called "neural cy or "self-organizing systems,".

He then solves the simpler problem and uses the information obtained in the solution of the simpler problem in the solution of the original complex problem.

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.

An early expression of this difficulty is the socalled might be stated that Socrates's birthplace was Athens.