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.

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.

MEAN ARTERIAL PRESSURE is ACCEPTABLE (currently one hour).

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

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.

We have developed and partially Imp;zmented an "automated consultant" called SACON (Structural Analysis CONsultant), using the EMYCIN system as Its framework. SACON advises non expert engineers in the use of a large, general-purpose structural analysis program. The structure of the knowledge b,:se, including the major concepts used and Inferences drawn by the consultant, is presented. We conclude by making some observations 11 light of this application about the EMYCIN system as a representational vehicle and the process of acquiring knowledge for rule-based systems. Key words: knowledge-based systems, knowledge acquisition, knowledge representation, automated consultant, structural analysis, inference structure. This research was supported by the Defense Advanced Research Projects Agency (ARPA Order No. 2494 Contract No. DAHC15-73-C-0435) and the Air Force Flight Dynamics Laboratory. Reprinted from the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, August 1979. Used by permission of the International Joint Conference on Artificial Intelligence, Inc.; copies of the Proceedings are available from Morgan Kaufmann Publishers, Inc., 95 First Street, Los Altos, CA 94022, USA.

If no CONTROL slot is associated with a prototype, the Interpreter will attempt to fill in values for the prototype components in the order of their Importance measures. When all of the clauses in the CONTROL slot have been executed and the prototype has been instantiated, a decision is madel as to whether the prototype should be confirmed as matching the data in the case. The system then checks either the IF-CONFIRMED slot or the IF-DISPROVED slot to determine what should be done next. Similarly, the ACTION slot specifies stops to be taken for a confirmed prototype during the clean-up stage.

These links are based on domain facts and relations (e g. that a topic is the name of a block of Step?: Discuss the final hypothesis for the goal

EMYCIN is a programming system for writing knowledge-based consultation programs with a production-rule representation of knowledge. Several major components of the system, Including an explanation program and knowledge acquisition routines, are described. EMYCIN has been used to build consultation systems in several areas of medicine, as well as an engineering domain. These experiences lead to some general conclusions regarding the potential applicability of EMYCIN to new domains. Keywords: knowledge-based systems, production rules, knowledge representation, automated consultant.