The trend towards increased use of symoolic reasoning techniques for clinical decision programs stems from the dual goals of improving the performance and increasvlg the acceptance of sucn systems.

This report presents BAOBAB-2, a computer program built upon MYCIN [Shortliffe, 1974] that Is used for understanding medical summaries describing the status of patients. Due both to the conventional way physicians present medical problems In these summaries and the constrained nature of medical jargon, these texts have a very strong structure. BAOBAB-2 takes advantage of this structure by using a model of this organization as a set of related schemes that facilitate the Interpretation of these texts. Structures of the schemes and their relation to the surface structure are described. Issues relating to selection and use of these schemes by the program during interpretation of the summaries are discussed.

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques Automatic Programming (AP) Is a new, dynamic, and not precisely defined area of artificial intelligence. This overview discusses the definitions, history, motivating forces and goals of automatic programming and includes a brief description of the basic characteristics and central issues of AP systems. The article begins with a section discussing the various possible definitions of automatic programming, the background in which it has achieved existence, as well as some of its general motivating forces and goals. The next section describes four characteristics of all AP systems: the method by which a user of such a system specifies or describes the desired program, the target language in which the system writes the program, the problem or application area to which the system is addressed, and the approach or operational method employed by the system. Next, a section discusses four basic issues, one or more of which concern all AP systems: the representation and processing of partial or incomplete information; the transformation of structures, and especially the transformation of program descriptions into other descriptions (in this chapter, the term program description includes the user's specification of the desired program, any Internal representations of the progrrm, as well as the target language implementation); the efficiency of the target language Imp,ementation; and the system's capabilities for aiding in the understanding of the program.

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Overview There are two main area.

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.

It is noted that no one method is best with a view to the potential role of automated decision for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge aids in that domain 1611.

Each of Since the early 1970's, researchers on computei-- these programs uses a representation scheme, based medical reasoning have begun to recognize known as production rules [3], to encode the the potential benefits of applying symbolic reasoning medical knowledge used for decision making.

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.

The long term practical goal of this work is the creation of a fully automated system for protein structure determination, starting with data collection and ending with an accurate 3-D model of the molecule. Most of the software already exists at the two ends of the process. At the "front end", programs exist for reducing and transforming the x-ray diffraction data, and estimating phases. The result of this processing Is an electron density map (EDM), which gives a blurred view of the electron cloud surrounding the molecule. At the "back end" are a variety of numerical techniques for taking a full or partial model of the protein and iteratively refining the atomic coordinates so that the model is a best compromise between one which best fits the data and one which best matches Ideal stereochemical constraints [Hermans74] [Agarwal77].

Intelligent systems can explore only tiny subsets of their potential external and conceptual worlds. To increase their effective capacities, they must develop efficient forms of representation, access, and operation. In this paper we develop several techniques which do not sacrifice expressibility, yet enable programs to (semi.