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

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

This booklet constitutes the refereed lawsuits of the tenth overseas convention on synthetic 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 publication. The papers deal with a vast spectrum of issues in AI, together with traditional language processing, computational studying, desktop studying, AI making plans, heuristics, neural info processing, adaptive structures, computational linguistics, multi-agent platforms, AI good judgment, wisdom administration, and data retrieval.

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

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Section 3 shows how machine learning techniques can be used to automatically build loop unrolling heuristics. Section 4 illustrates an implementation of the technique based on the OC1 decision tree software [10]. 2 Loop Unrolling as a Case The performance of superscalar processors relies on a very high frequency1 and on the parallel execution of multiple instructions (this is also called Instruction Level Parallelism –ILP). To achieve this, the internal architecture of superscalar microprocessors is based on the following features: Memory hierarchy: the main memory access time is typically hundreds of times greater than the CPU cycle time.

X1n  ..   .   1 . . Xmn  ..   . mm . . Xmn This example gives one possible application of our formalism. In this paper, we will present the operator of generalization and the training algorithm. 3 Definitions An interval x can be identified by these two ends [x− , x+ ]. In this case, it is called: instantiated interval. Definition 1. A generalized interval X is defined by a sequence of instantiated intervals or by a sequence of intervals and a constraints network between these intervals.

The resulting IBMBS of VS (I + , I − ∪ {i}) are formed from the set I + and the maximal boundary sets G(I + , {n}) for all n ∈ I − ∪ {i}. The worst-case time complexity of the algorithm, given one positive instance and one negative instance, is O(t↑n + (|I + | + |I − |)Γn tm ). 2 25 Retraction Algorithm The retraction algorithm of the IBMBS updates the representation when an instance is removed from one of the training sets. It is correct for the class of admissible concept languages when the property G holds [7].

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