To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives. In Artificial Intelligence: A Knowledge-Based Approach (Boyd and Fraser, Boston, 740 pp., $48.95), author Morris W. Firebaugh has succeeded in meeting each of these requirements.

The main difficulties in knowledge acquisition from domain experts stem from the variety of forms of knowledge, the various representations of knowledge, and the problems in making these explicit and accessible. There is, at present, no systematic overall methodological framework for knowledge acquisition to guide the organization and arrangement of the appropriate application of the many manual and automated techniques and methods used for knowledge acquisition. In considering these problems it is appropriate to draw on studies in cognitive science and associated disciplines to examine the models of the expert and the demands and goals of the task. This paper develops the modeling processes involved from the perspective of the expert trying to communicate his view of a target system and transfer it into computer implementable form. It identifies the distinct processes of elicitation, analysis and implementation, the knowledge representations of the intermediate knowledge bases which can be used to help the expert review and refine his conceptual model, and the computer knowledge bases which may be unrecognizable by the expert as related to his developing models.

We discuss in this paper architectures for executing probabilistic rule-bases in a parallel manner, using as a theoretical basis recently introduced information-theoretic models. We will begin by describing our (non-neural) learning algorithm and theory of quantitative rule modelling, followed by a discussion on the exact nature of two particular models. Finally we work through an example of our approach, going from database to rules to inference network, and compare the network's performance with the theoretical limits for specific problems.

ABSTRACf The Space Environment Laboratory in Boulder has collaborated with the University of Colorado to construct a small expert system for solar flare forecasting, called THEa. It performed as well as a skilled human forecaster. We have constructed TheoNet, a three-layer back-propagation connectionist network that learns to forecast flares as well as THEa does. TheoNet's success suggests that a connectionist network can perform the task of knowledge engineering automatically. A study of the internal representations constructed by the network may give insights to the "microstructure" of reasoning processes in the human brain.

ABSTRACf The Space Environment Laboratory in Boulder has collaborated with the University of Colorado to construct a small expert system for solar flare forecasting, called THEa. It performed as well as a skilled human forecaster. We have constructed TheoNet, a three-layer back-propagation connectionist network that learns to forecast flares as well as THEa does. TheoNet's success suggests that a connectionist network can perform the task of knowledge engineering automatically. A study of the internal representations constructed by the network may give insights to the "microstructure" of reasoning processes in the human brain.

We discuss in this paper architectures for executing probabilistic rule-bases in a parallel manner, using as a theoretical basis recently introduced information-theoretic models. We will begin by describing our (non-neural) learning algorithm and theory of quantitative rule modelling, followed by a discussion on the exact nature of two particular models. Finally we work through an example of our approach, going from database to rules to inference network, and compare the network's performance with the theoretical limits for specific problems.

In recent years connectionist (sometimes called "non-symbolic"

We discuss in this paper architectures for executing probabilistic rule-bases in a parallel manner,using as a theoretical basis recently introduced information-theoretic models. We will begin by describing our (non-neural) learning algorithm and theory of quantitative rule modelling, followed by a discussion on the exact nature of two particular models. Finally we work through an example of our approach, going from database to rules to inference network, and compare the network's performance with the theoretical limits for specific problems.

The two-volume set entitled "Knowledge-Based Systems (Volume 1, Knowledge Acquisition for Knowledge-Based Systems, 355 pp., and Volume 2, "Knowledge Acquisition Tools for Expert Systems, 343 pp., Academic Press, San Diego, California, 1988), edited by B. R. Gaines and J. H. Boose, is an excellent collection of papers useful to both commercial practitioners of knowledge-based-systems development and research-oriented scientists at specialized centers or academic institutions.

The Advanced Architectures Project at Stanford University's Knowledge Systems Laboratory seeks to gain higher performance for expert system applications through the design of new, innovative software and hardware architectures. This research concentrates particularly on the use of parallel machines to gain speedup and the design of the software to exploit emergent paral-lel hardware architectures. This article describes the project and details its goals and the work performed in the pursuance of these goals. A brief description is given of each of the project components, and a complete bibliography appears of the publications produced for the project.