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.

Abstract: Techniques for discovering rules by Induction from large collections of Instances are developed. These are based on an Iterative scheme for dividing the Instances Into two sets, only one of which needs to be randomly accessible. These techniques have made It possible to discover complex rules from data bases containing many thousands of Instances. Results of several experiments using them are reported.

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

This pc)er presents BAOBAB-2, a computer program built around MYCIN [Shortliff e, 1974] that is used for understanding medical summaries describing the status of patients. Due to the stereotypic way the physicians present medical problems in these summaries in addition to the constrained nature of medical jargon, these texts have a very strong structure. BAOBAB-2 takes advantage of these structures by having a model of this organization as a set of related schemas 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 ara discussed.

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.

In Al knowledge representation schemes, structures that describe other structure:: are said to represent "meta-knowledge." Knowledge about other knowledge can be either about the form of the representation scheme itself (e.g., its syntax) or about the "facts" that are represented (their origin, reliability, Importance, etc.). After reviewing the use of explicit meta-knowledge In several systems, some studies of human behavior that Indicate people's ability to reason about what they know and about how they reason are described. The concept of meta-level knowledge captures intrinsic, commonplace properties of human cognition that are central to an understanding of memory and Intelligence. The use of meta-knowledge In Al systems like MYCIN, which have reached humanexpert-level performance In complex domains, Is a key breakthrough In the design of "knowledge-based" Intelligent systems. Meta-level knowledge has been used in these systems primarily in the implementation of "introspective" processes: Acquisition of new knowledge and explanation of the system's reasoning to users. The usefulness of meta-level descriptions for these and other functions has prompted proposals for their incorporation in several new general-purpose representation schemes, like KRL, as described In the next section.

As the patient setting changes--e.g., as a patient starts Lo breathe on his own during removal (weaning) from the ventilator--the same measurement values lead to different interpretations. In order to properly interpret data collected during changing therapeutic contexts, the knowledge base includes a model of the stages that a patient follows from admission to the unit through the end of the critical monitoring phase. Recognition of the appropriate patient context is an essential step in determining the meaning of most physiological measurements. The program maintains a description of the current and optimal ventilatory therapies for any given time. The list of states and possible state transitions are represented in Figure 1.