Artificial Intelligence: Methodology, Systems, and by David House, Björn Granström (auth.), Donia Scott (eds.)

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

This e-book constitutes the refereed complaints of the tenth overseas convention on synthetic Intelligence: technique, structures, 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 e-book. The papers tackle a large spectrum of subject matters in AI, together with ordinary language processing, computational studying, desktop studying, AI making plans, heuristics, neural details processing, adaptive structures, computational linguistics, multi-agent structures, AI common sense, wisdom administration, and knowledge retrieval.

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Extra 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].

Generalization of an example X compared to Y knowing the counterexample Z. disc disc disc int disc cont disc eq disc cont−1 disc inter−1 disc disc−1 inter disc inter int inter cont inter eq inter cont−1 inter inter−1 inter disc−1 cont disc cont int cont cont cont eq cont cont−1 cont inter−1 cont disc−1 5 disc ok ok ok ok ¬ok ¬ok ¬ok 0 0 0 0 ¬ok ¬ok ¬ok 0 0 0 0 0 0 ok inter ok ok ok 0 0 0 0 0 ok ok ok 0 ok 0 0 0 ok 0 0 ok ok cont−1 ok ok ok 0 0 0 0 0 ok ok 0 0 0 0 0 0 ok 0 0 0 0 inter−1 ok 0 0 0 0 0 0 ok ok 0 0 0 0 0 ok ok ok 0 0 0 0 disc−1 ok 0 0 0 0 0 0 ok 0 0 0 0 0 0 ok 0 0 0 0 0 0 −1 cont disc cont−1 int cont−1 cont cont−1 eq cont−1 cont−1 cont−1 inter−1 cont−1 disc−1 inter−1 disc inter−1 int inter−1 cont inter−1 eq inter−1 cont−1 inter−1 inter−1 inter−1 disc−1 disc−1 disc disc−1 int disc−1 cont disc−1 eq disc−1 cont−1 disc−1 inter−1 disc−1 disc−1 disc 0 0 0 0 ¬ok ¬ok ¬ok 0 0 0 0 0 0 ok 0 0 0 0 0 0 ok inter 0 0 0 0 ok ok ok 0 0 0 0 0 ok ok 0 0 0 0 0 0 ok cont−1 0 ok ok ok ok ok 0 0 0 ok 0 0 ok 0 0 0 ok 0 0 ok ok inter−1 0 ok 0 0 ok 0 0 0 ok ok ok ok ok 0 0 0 ok 0 0 ok ok disc−1 ¬ok ¬ok 0 0 ¬ok 0 0 ¬ok ¬ok 0 0 ¬ok 0 0 ¬ok ¬ok ok ok ¬ok ok ok Learning Algorithms This section presents two training algorithms: a naive learning algorithm (NLAGI) and an optimized learning algorithm (OLAGI) which reduce the training time.

Fig. 4. The Classification Algorithm. The classification algorithm of the IBMBS is given in figure 4. Given a nonempty version space VS (I + , I − ), it classifies an instance i ∈ I in two steps. In the first step the algorithm forms the IBMBS of the version space VS (I + , I − ∪ {i}) using the learning algorithm applied on the IBMBS of VS (I + , I − ) with the instance i labeled as negative. If VS (I + , I − ∪ {i}) is empty, by theorem 14 all the descriptions in VS (I + , I − ) cover the instance.

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