Section 1 1 Introduction The MOLGEN project has focused on research Into the applications of symbolic computation and Inference -to the field of molecular biology. This has taken the specific form of systems which provide assistance to the experimental scientist in various tasks, the most important of which have been the design of complex experiment plans and the analysis of nucleic acid sequences. During the period of further research proposed in this document, we plan to expand and improve these systems and build new ones to meet the rapidly growing needs of the domain of recombinant DNA technology. We do this with the view of including.

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.

This talk is about "knowledge representation," particularly some of the fundamental assumptions in' olved in the way we handle knowledge in current Al and cognitive science research. The whole enterprise seems to have at-.'andoned Namely, we have a:7ccd to assume that knowledge is something that can be represelted--that knowing something means having a data structure stored away that stands for what is known. In other words, we treat knowledge as an object, a representable object. The advantage of this way of looking at things is that there is a very simple relationship between the things we say a person or system knows, and the memories, knowledge, or data structures we say he or it has stored.

MEAN ARTERIAL PRESSURE is ACCEPTABLE (currently one hour).

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.

For example, a for an integrated database requires each pctential relation in a user view may be a'JOIN' of two user or application to specify its view as a data

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.