In response to this by Denker with an impressive series need, Shelby Lyman, the host of past of moves that ended in a sacrifice Public Broadcasting Station (PBS) which brought home the win. Everyone series on world chess championship seemed impressed with Hitech's matches, organized the AGS Challenge performance throughout the match, Match at the New School for although it was clear the audience Social Research in New York City. Denker Funding for this event was provided was generous in his praise of Hitech, by AGS Computers, Inc., a New Jersey-based saying, "The machine gave me a real software firm. The match trimming; I am very impressed." The was held September 22-25, with one final game of the match follows with game played each day, and was widely a few annotations.

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We argue that the time between mission conception and implementation can be radically reduced, that launch mass can be slashed, that totally autonomous robots can be more reliable than ground controlled robots, and that large numbers of robots can change the tradeoff between reliability of individual components and overall mission success. Lastly, we suggest that within a few years it will be possible at modest cost to invade a planet with millions of tiny robotsJournal of The British Interplanetary Society, Vol. 42, pp 478-485

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.

Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell--for handling a domain model expressed by a causal probabilistic network. The only topological restriction imposed on the network is that, it must not contain any directed loops. The approach is illustrated step by step by solving a. genetic breeding problem. A graph representation of the domain model is interactively created by using instances of the basic network components—nodes and arcs—as building blocks. This structure, together with the quantitative relations between nodes and their immediate causes expressed as conditional probabilities, are automatically transformed into a tree structure, a junction tree. Here a computationally efficient and conceptually simple algebra of Bayesian belief universes supports incorporation of new evidence, propagation of information, and calculation of revised beliefs in the states of the nodes in the network. Finally, as an example of a real world application, MUN1N an expert system for electromyography is discussed.IJCAI-89, Vol. 2, pp. 1080–1085

Proc. Eleventh Intl. Joint Conference on Artificial Intelligence (pp. 731-737). Detroit, MI: IJCAII.

In IJCAI-89, pp. 334–340.

Proc. Eleventh Intl. Joint Conference on Artificial Intelligence (pp. 681-687). Detroit, MI: IJCAII.

In IJCAI-89, Vol. 1, pp. 290-296.