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

ISBN-10: 3540441271

ISBN-13: 9783540441274

ISBN-10: 3540461485

ISBN-13: 9783540461487

This ebook constitutes the refereed lawsuits of the tenth overseas convention on man made Intelligence: technique, platforms, and Appliations, AIMSA 2002, held in Varna, Bulgaria in September 2002.
The 26 revised complete papers provided including 2 invited papers have been rigorously reviewed and chosen for inclusion during this booklet. The papers deal with a vast spectrum of subject matters in AI, together with ordinary language processing, computational studying, desktop studying, AI making plans, heuristics, neural info processing, adaptive structures, computational linguistics, multi-agent structures, AI common sense, wisdom administration, and knowledge retrieval.

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

Example text

If the set I − is nonempty, then: (VS (I + , I − ) = ∅) ↔ (∀n ∈ I − )(G(I + , {n}) = ∅). The version-space collapse algorithm uses corollary 13 and is given in figure 3. If a version space VS (I + , I − ), given by IBMBS, is checked for collapse, the algorithm visits the sets G(I + , {n}) for n ∈ I − . If none of the sets G(I + , {n}) is empty, by corollary 13 VS (I + , I − ) is not empty and the algorithm returns false. Otherwise, by corollary 13 VS (I + , I − ) is empty and the algorithm returns true.

M. Long, and A. Srinivasan. Approximating hyperrectangles: Learning and pseudo-random sets. In ACM. Symposium on the Theory of Computing, pages 314–323, 1997. S. A. Auster. A study of thinking. In New York: John Wiley, 1956. P. -F Condotta, L. Fari˜nas del Cerro, and A. Osmani. Reasoning about generalized intervals. In LNAI 1480, pages 50–61. Springer, AIMSA-98, 1998. F. Hoppner. Discovery of temporal patterns -learning rules about the qualitative behaviour of time series. In LNAI 2168 PKDD, pages 192–203.

Polynomial-Time Learning with Version Spaces. In: Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI Press, Menlo Park, CA (1992) 117-122 3. : Version Spaces without Boundary Sets. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI Press, Menlo Park, CA (1997) 491-496 4. : Demand Networks: An Alternative Representation of Version Spaces. Master’s Thesis, Department of Computer Science and Systems Sciences, Stockholm University, Stockholm, Sweden (1990) 5.

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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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