An approach to query optimization is described that draws on two sources of knowledge: real world constraints on the values for the application domain served by the database; and knowledge about the current structure of the database and the cost of available retrieval processes. Real world knowledge is embodied in rules that are much like semantic integrity rules. The approach, called "query rephrasing", is to generate semantic equivalents of user queries that cost less to process than the original queries. The operation of a prototype system based on this approach is discussed in the context 0. simple queries which restrict a single file. The need for heuristics to limit the generation of equivalent queries is also discussed, and a method ut g "constraint thresholds" derived from

Because this paper is about computer programs thal generate explanations, my debt to Prof. Hempel will be obvious. However, insofar as I wish to use the term'discovery' to cover the activity of finding explanations, I know that Prof. Hempel will not entirely agree with these ideas about mechanizing the activity. The purpose of this paper is to elaborate a very simple idea: that discovery in science and medicine can be profitably viewed as systematic exclusion of hypotheses. That is, hypotheses that explain empirical data can be found systematically by methods that can be implemented in computer programs. The conditions under which this view makes sense are an important part of the elaboration. Two necessary conditions are that the space of relevant hypotheses is definable and that there exist criteria of rejection and acceptability. Because the space of hypotheses is immense for most interesting problems, it is also desirable that there exist criteria for guiding a systematic search.

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.