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inference engine are the goal stack (GS) and the working storage (WS). The goal stack is a stack structure that contains the goals and subgoals that the IE must infer by using the rule base or querying the user. WS contains facts that have been inferred or provided by the user. This report is a manual for users of the General InfereoPqo^-e for Expert System (GIEES) implemented in PROLOG. GIEES has capabilities to explain its reasoning to accommodate uncertainties and to manage the knowledge base it uses for reasoning. GIEES comprises six modules for: command interpretation and dispatching, reasoning and inferencing, manipulation of knowledge bases, explanation or reasoning to the user, general support functions, and user help.

Expert Systems to Aid in Wind Farm Operations Sehluter, L. L., Nateghian, F., Luger, G. F. Sandia National Labs., Albuquerque, NM. Corp. Source Codes: 068123000; 9511100 Sponsor: Department of Energy, Washington, DC. Report No.: SAND-91-1436C; CONF-920122-3 1991 9p Languages: English Document Type: Conference proceeding Journal Announcement: GRAI9207; ERA9213 American Society of Mechanical Engineers (ASME) energy sources technology conference and exhibition, Houston, TX (United States), 26-30 Jan 1992. Sponsored by Department of Energy, Washington, DC. NTIS Prices: PC A02/MF AO1 Country of Publication: United States Contract No.: AC04-76DP00789 An expert system is a knowledge-based program that

provides solutions to problems in a specific domain by mimicking the behavior of a human expert. Expert systems can have several advantages over traditional programming methods; however, developing an expert system generally involves a considerable amount of time and money. Therefore, careful investigation must be done to ensure that a problem is suited for an expert system application. This paper examines several areas where an expert system may help wind farm operators lower their operational costs. Justifications for using expert systems rather than traditional programming methods are given. This paper also discusses some of the design decisions that were made in developing an expert system for US Windpower that will aid in diagnosing wind turbine failures. 11 refs., 3 figs.

Distributed Agent Architecture for Real-Time Knowledge-Based Systems: Real- Time Expert Systems Project, Phase 1 (Final Report)

Lee, S. D. Research Inst. for Advanced Computer Science,

Moffett Field, CA Corp. Source Codes: 095294000; RR454545 Sponsor:. National Aeronautics and Space Administration, Washington, De. Report No.: NAS 1.26:188947; NASA-CR-188947 May 90 58p Languages: English Journal Announcement: GRAI9206; STAR3003 NTIS Prices: PC A04/MF A01 Country of Publication: United States Contract No.: NCC9-16; RICIS PROJ. SE-19 We propose a distributed agent architecture (DAA)

that can support a variety of paradigms based on both traditional real-t ime computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide recta-level control.

Instructor's Plan: A Lesson Planning Expert System for School Teachers

Wilkins, D. A. Corp. Source Codes: 888888888 1990 4p Languages: English Journal Announcement: GRAI9206 Available from ERIC Document Reproduction Service (Computer Microfilm International Corporation), 3900 Wheeler Ave., Alexandria, VA 22304-5110. NTIS Prices: Not available NTIS Country of Publication: United States The product of several years of research and

development at Brigham Young University, Instructor's Plan (IP) is an expert system for rapid lesson design and authoring. Its primary audience is preservice and inservice school teachers. It will run on IBM PC, XT,

NTIS Section 331

AT, and PS/2 systems and compatibles with a color monitor and at least one floppy disk drive. IP's central feature is its expert system, which possesses two primary components: a knowledge base and an inference engine. Founded on instructional theory that proposes that specific learning eruditions require different instructional strategies or strategy modifications in order to optimize learning, the knowledge base contains both production rules and instructional strategies. The instructor can access directly and make modifications to the instructional strategy aspect. The inference engine manages the knowledge base and the inferencing process, governing the questions posed to the instructor and the searching of rules, and testing the goals of the expert system to see if they have been accomplished. IP considers two main categories of learning conditions when determining the best instructional strategymleaming outcome and instructional mode--and identifies eight learning outcomes: response, recitation, explanation, classification, prediction, decision, performance, and problem solving. The instructional mode refers to whether the lesson is more instructor or learner centered and controlled. Formative evaluation has shown that instructors find IP very easy to learn to use, and they report that it reduces planning time and improves the quality of their lesson design. (11 references) (BBM).

Automating Instructional Systems Development Tennyson, R. D., Christensen, D. L. Corp. Source Codes: 888888888 1991 38p Languages: English Journal Announcement: GRAI9206 Available from ERIC Document Reproduction Service (Computer Microfilm International Corporation), 3900 Wheeler Ave., Alexandria, VA 22304-511O. NTIS Prices: Not available NTIS Country of Publication: United States This paper presents framework specifications for an

instructional systems development (ISD) expert system. The goal of the proposed ISD expert system is to improve the means by which educators design, produce, and evaluate the instructional development process. In the past several decades, research and theory development in the fields of instructional technology and cognitive science has advanced the knowledge base for instructional design theory such that learning and thinking can be significantly improved by direct instructional intervention. Unfortunately, these advancements have increased the complexity of employing instructional design theory, making instructional development both costly and time consuming. It is proposed that through the application of expert system methods, it is now possible to develop

an intelligent computer-based ISD expert system that will enable educators to employ instructional design theory for curricular and instructional development. Presented in this paper is a framework for the development of an ISD expert system that will assist both experienced and inexperienced instructional developers in applying advanced instructional design theory. Diagrams of three generat ions of the proposed model are appended. (38 references) (Author/BBM).

NPA development Kim, D. S., Chae, S. K., Chang, W. P. Korea Atomic Energy Research Inst., Daeduk (Republic of Korea). Corp. Source Codes: 102514000; 3634500 Report No.: KAERI/RR-902/89 Jun 90 315p Languages: Korean Journal Announcement: GRAI9205 U.S. Sales Only. NTIS Prices: PC A14/MF A03 Country of Publication: Korea, Republic of The operator's response to a nuclear plant emergency

situation depends on the system transient behavior even though the initiation of the transient is recognized, so that it is necessary for operators to predict how the plant will go on and what the unmeasurable safety parameters will be. In order to answer these concerns, NPA, which simulates system behaviors fast and realistically, would be one of the desirable options. For this reason, the objective of this research program is primarily to develop a convenient and efficient NPA that could help the operators decide the operating procedures by simulating the system with fast running mode during plant transients. (author). (Atomindex citation 22:044670)

Techniques for Knowledge Acquisition (Research note)

Cosgrove, S. J. Electronics Research Lab., Adelaide (Australia). Corp. Source Codes: 056162000; 410863 Sponsor: Depertment of Defence, Canberra (Australia). Report No.: ERL-0553-RN; DODA-AR-006-715 Jan 91 18p Languages: English Journal Announcement: GRAI9205 NTIS Prices: PC A03/MF A01 Country of Publication: Australia Knowledge acquisition is commonly regarded as the

major bottle-neck in Expert System (ES) development. Numerous techniques and commercial packages have been built in order to widen this bottle-neck. Unfortunately, these have had little success, except in simple, highly confined domains. Therefore, knowledge