Advances in Neural Information Processing Systems 9 by Michael C. Mozer, Michael I. Jordan, Thomas Petsche

By Michael C. Mozer, Michael I. Jordan, Thomas Petsche

The once a year convention on Neural info Processing structures (NIPS) is the flagship convention on neural computation. It attracts preeminent educational researchers from world wide and is greatly thought of to be a exhibit convention for brand new advancements in community algorithms and architectures. The wide variety of interdisciplinary study components represented comprises neural networks and genetic algorithms, cognitive technology, neuroscience and biology, computing device technology, AI, utilized arithmetic, physics, and plenty of branches of engineering. simply approximately 30% of the papers submitted are approved for presentation at NIPS, so the standard is tremendously excessive. the entire papers offered seem in those complaints.

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In particular, the notion of expla­ nation is not captured adequately by set-theoretic isomorphism (d. van Fraassen, 1980). 3. 1. Rules and Concepts Chapter 2 described how PI represents knowledge using rules organized by concepts. I now want to show how such structures can contribute to an account of the nature of scientific knowledge that is both reasonably precise and suffiCiently rich to account for the various roles of scientific theories. Focusing on rules alone might suggest that in PI theories are nothing more than syntactic structures akin to the sets of sentences that, according to the logical positivists, constitute scientific theories.

Schema) that describes typical behavior in a supermarket. The slots in the SUPERMARKET frame are matched with the information provided in the sentence, and then the additional information contained in the frame can be used to answer questions about John's behavior described in the sentence.

Why does PI use messages, rules, and concepts with so much structure? The justification for complicating these structures is simply to be able to use them in complex processes: they support a far more elaborate and interesting model of problem solving and inductive inference than would otherwise be possible. The question of whether a computational model of thinking must have separate structures corresponding to concepts is con­ troversial, and the important cognitive architectures of Anderson (1983) and Laird, Rosenbloom, and Newell ( 1986) do not have them.

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