It has been argued by Hartley and Sleeman.

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.

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.

I cis is the final report for Contract N-00014-79-C-03C2, covering the period of 15 March 1979 through 14 March 1982. The goal of the project was to develop methods for representing teaching and problem-solving knowledge in computer-based tutorial systems. One focus of the work was formulation of principles for managing a case method tutorial dialogue; the other major focus was investigation of the use of a production rule representation for the subject material of tutorial program. The main theme pursued by this research is that representing teaching and problemsolving knowledge separately and explicitly enhances the ability to build, modify and test complex tutorial programs. Two major corr Jter programs were constructed.

No. STAN-CS-82-909 Also numbered: HPP42-7 Department of Computer Science Stanford University Stanford, CA 94305 Abstract This paper describes the student modeler of the GUIDON2 tutor, which understands plan: by a dual search strategy. It first produces multiple predictions of student behavior by a model-driven simulation of the expert. Focused, data-driven searches then explain incongruities. By supplementing each other, these methods lead to an efficient and robust plan understander for a complex domain. Diagnostic problem-solving requires domain knowledge and a plan for applying that knowledge to the problem.

The Key idea is to represent explicitly and separately: a A. The limitations of MYCIN for application to teaching domain-independent diagnostic strategy in the form of meta-rules,

This paper briefly outlines the difference between Branching on the bas of response was the first step coward traditional instructional programs and MAI.

There are several different types of goals, and each has a corresponding set of parameters. For example, the goal (obtain (coefficient 6 x 2)) means to obtain an expression for the coefficient of x2 in g6, either to print it out or pass as argument to some MACSYMA command. Note that this can be done either by finding an already computed expression (stored, say, as the value of some variable) or by computing it anew. Either implementation is satisfactory so long as it computes the desired expression.

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Natural Language Processing Ovnrview The most common way that human beings communicate Is by speaking or writing In one of the "natural" languages, like English, French, or Chinese. Computer programming languages, on the other hand, seem awkward to humans. These "artificial" languages are designed to have a rigid format, or syntax, so that a computer program reading and compiling code written In an artificial language can understand what the programmer means. In addition to being structurally simpler than natural languages, the artificial languages can express easily only those concepts that are important In programming: "Do this then do that," "See it such and such Is true," etc. The things that can be expressed In a language are referred to as the semantics of the language. The research on understanding natural language described in this section of the Handbook is concerned with programs that deal with the full range of meaning of languages like English.

Those of us involved In the creation of the Handbook of Artificial Intelligence, both writers and editors, have attempted to make the concepts, methods, tools, and main results of artificial Intelligence research accessible to a broad scientific and engineering audience. Currently, Al work Is familiar mainly to its practicing specialists and other interested computer scientists. Yet the field Is of growing interdisciplinary interest and practical Importance. With this book we are trying to build bridges that are easily crossed by engineers, scientists in other fields, and our own computer science colleagues. In the Handbook we Intend to cover the breadth and depth of Al, presenting general overviews of the scientific issues, as well as detailed discussions of particular to -hniques and Important Al systems.