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

The language designer typically designs that language with one particular application domain in mind: as subsequent types of applications are tned, what had originally been useful features are found to be undesirable limitations, and the language is overhauled or scrapped. One remedy to this bleak cycle might be to construct a representation language whose domain is the field of representational languages itself. One remedy to this bleak cycle might be to construct a representation language whose domain is th field of representation languages itself, a system which could then be tailored to suit many specific applications. Toward this end, we (Professor Douglas Lena and 1) have designed and implemented RLL-1, an object-centered2 Representation Languange Language.3 A representation language language (r11) must explicitly represent the components of representation languages in general and of itself in particular.

Cephalothin and play an educational role by reminding the health professional cephaloridine therapy for bacterial meningitis. Ann Intern Med. of critical factors to consider when prescribing therapy 1975; 82:689-93.

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.

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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