Artificial Intelligence: Methodology, Systems, and - download pdf or read online

By David House, Björn Granström (auth.), Donia Scott (eds.)

ISBN-10: 3540441271

ISBN-13: 9783540441274

ISBN-10: 3540461485

ISBN-13: 9783540461487

This publication constitutes the refereed complaints of the tenth overseas convention on man made Intelligence: method, platforms, and Appliations, AIMSA 2002, held in Varna, Bulgaria in September 2002.
The 26 revised complete papers awarded including 2 invited papers have been conscientiously reviewed and chosen for inclusion during this publication. The papers deal with a huge spectrum of subject matters in AI, together with usual language processing, computational studying, computer studying, AI making plans, heuristics, neural details processing, adaptive platforms, computational linguistics, multi-agent structures, AI common sense, wisdom administration, and data retrieval.

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Additional resources for Artificial Intelligence: Methodology, Systems, and Applications: 10th International Conference, AIMSA 2002 Varna, Bulgaria, September 4–6, 2002 Proceedings

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C Springer-Verlag Berlin Heidelberg 2002 42 A. Monsifrot, F. Bodin, and R. Quiniou papers have specifically addressed the issue of building such heuristics. Nevertheless approximate heuristics have been proposed [8,11] for unroll and jam a transformation that is like unrolling (our example) but that behaves differently. Our study aims to simplify compiler construction while better exploiting optimizations. To evaluate the potential of this approach we have chosen a simple transformation: loop unrolling [6].

Hence, the concept-learning task in this case is to find descriptions of C in Lc. To find the descriptions of a target concept, we specify them by the consistency criterion: a description c is consistent iff c correctly classifies training data. The set of all consistent descriptions is called the version space [5]. Definition 1. (Version Space) Given training sets I + and I − of a target concept, the version space VS (I + , I − ) is defined as follows: VS (I + , I − ) = {c ∈ Lc | (∀i ∈ I + )M (c, i) ∧ (∀i ∈ I − )¬M (c, i)}.

Ligozat. A new proof of tractability for ORD-HORN relations. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 395–401, 1996. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 305–310, 1977. A. Osmani and F. Lévy. Generation d’une base d’apprentissage pour l’apprentissage de pannes dans un reseau de télécommunications. In (RFIA’2000). A. Osmani. Diagnostic à base de modèles pour la supervision de pannes dans un réseau de télécommunications & raisonnement sur les intervalles.

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Artificial Intelligence: Methodology, Systems, and Applications: 10th International Conference, AIMSA 2002 Varna, Bulgaria, September 4–6, 2002 Proceedings by David House, Björn Granström (auth.), Donia Scott (eds.)


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