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.

Universal plans address the tension between reasoned behavior and timely response by caching reactions for classes of possible situations. This technique reduces the average time required to select a response at the expense of the space required to store the cache-the classic time-space trade-off. In his article, Matthew Ginsberg argues from the time extreme and against the space extreme. Although I find both extremes undesirable, I defend an increase in space consumption.

Artificial Intelligence is solving more and more real world problems, but penetration into the complexities of government administration has been minimal. The author suggests that combining expert system technology with conventional procedural computer systems can lead to substantial efficiencies. Business rules can be removed from business-oriented computer systems and stored in a separate but integrated knowledge base, where maintenance will be centralized. Fourteen specific practical applications are suggested.

ABSTRACT Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of compet- ing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems. Artificial Intelligence, 40 (1-3), 235-82.

A fundamental goal of computer vision is the development of systems capable of carrying out scene interpretation while taking into account all the available knowledge. In this article, we focus on how the interpretation task can be aided by the expected scene information (such as map knowledge), which, in most cases, would not be in registration with the perceived scene. The proposed approach is applicable to the interpretation of scenes with three-dimensional structures as long as it is possible to generate the equivalent two-dimensional orthogonal or perspective projections of the structures in the expected scene. The system is implemented as a two-panel, six-level blackboard and uses the Dempster-Shafer formalism to accomplish inexact reasoning in a hierarchical space. Inexact reasoning involves exploiting, at different levels of abstraction, any internal geometric consistencies in the data and between the data and the expected scene. As they are discovered, these consistencies are used to update the system's belief in associating a data element with a particular entity from the expected scene.

Sometimes less ambiguous, are extremely complex.

Don was one of the pioneers the checkers player, and Waterman's. of our field, whose early research built the foundation for the "His subsequent contributions to protocol analysis, to area that would later come to be labeled "knowledge based the technology of rule-based systems, and to the literature of systems" (and still later "expert systems"). Don received a B.S. in Electrical Engineering from With Don's work on production systems in his thesis, it Iowa State University in 1958, and an M.S. in Electrical was only natural that he should move to Carnegie-Mellon to Engineering from the University of California, Berkeley in work with Allen Newell after acquiring his Ph.D. in 1968. He then entered the Ph.D. program at Stanford's Al takes up the story from there: newly created Cotiputer Science Department. While at "Don came to CMU in Psychology, rather than Computer Berkeley he met a young professor named Ed Feigenbaum, Science. As with many people in AI, he had an abiding and when Feigenbaum moved to Stanford in 1965 Don became interest in understanding human cognition, although it always Ed's first Ph.D. student.

The Oak Ridge National Laboratory (ORNL) Center for Engineering Systems Advanced Research (CESAR) is a national center for multidisciplinary long-range research and development (R&D) in machine intelligence and advanced control theory. Intelligent machines (including sensor-based robots) can be viewed as artificially created operational systems capable of autonomous decision making and action. One goal of the research is autonomous remote operations in hazardous environments. This review describes highlights of CESAR research through 1986 and alludes to future plans.

The annual conference of the Association for the Advancement of Artificial Intelligence (AAAI) is the premier U.S. gathering for artificial intelligence (AI) theoreticians and practitioners. On the commercial side, AAAI is the only event with a comprehensive exhibition that includes most significant U.S. vendors of AI products and services. In 1986 some 5100 people attended AAAI- a very good showing considering that the 1987 International Joint Conference on Artificial Intelligence (IJCAI) drew about the same number of people even with its substantial international support. The commercial exhibits at AAAI-86 (110 exhibitors; 80,000 square feet) gave us opportunity to take a snapshot of an industry in transition. What I saw was a dramatic increase in the commercialization of AI technology and a decrease in the mystique, smoke, and hype. A preliminary tour of the AAAI-86 exhibits indicated that participants could expect substantial changes from the situation at IJCAI-85.

We distinguish diagrammatic from sentential paper-and-pencil representationsof information by developing alternative models of information-processing systems that are informationally equivalent and that can be characterized as sentential or diagrammatic. Sentential representations are sequential, like the propositions in a text. Dlogrammotlc representations ore indexed by location in a plane. Dio-grommatic representations also typically display information that is only implicit in sententiol representations and that therefore has to be computed, sometimes at great cost, to make it explicit for use. We then contrast the computational efficiency of these representotions for solving several illustrative problems in mothe-matics and physics. When two representotions are informationally equivolent, their computational efficiency depends on the information-processing operators that act on them. Two sets of operators may differ in their copobilities for recognizing patterns, in the inferences they con carry out directly, and in their control strategies (in portitular. Diogrommotic ond sentential representations sup port operators that differ in all of these respects. Operators working on one representation moy recognize feotures readily or make inferences directly that are difficult to realize in the other representation. Most important, however, are differences in the efficiency of scorch for information and in the explicitness of information. In the representotions we call diagrammatic. Therefore problem solving con proceed through o smooth traversal of the diagram, and may require very little search or computation of elements that hod been implicit. "a picture is worth 10,OOO words" is a Chinese proverb. On inquiry, we find that the Chinese seem not to have heard of it, but the proverb is certainly widely known and widely believed in our culture. To understand why it is advantageous to use diagrams-and when it is-we must find some way to contrast diagrammatic and non-diagrammatic representations in an information-processing system.