Download AI*IA 2009: Emergent Perspectives in Artificial by Janneke H. Bolt (auth.), Roberto Serra, Rita Cucchiara PDF

By Janneke H. Bolt (auth.), Roberto Serra, Rita Cucchiara (eds.)

This e-book constitutes the refereed complaints of the eleventh foreign convention of the Italian organization for man made Intelligence, AI*IA 2009, held in Reggio Emilia, Italy, in December 2009.

The 50 revised complete papers offered including three invited talks have been conscientiously reviewed and chosen from eighty three submissions. The papers are prepared in topical sections on wisdom illustration and reasoning, desktop studying, evolutionary computation, seek, ordinary language processing, multi-agent platforms and application.

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Additional info for AI*IA 2009: Emergent Perspectives in Artificial Intelligence: XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia, Italy, December 9-12, 2009 Proceedings

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Looking now at PROVERBOX , we can see that search-based solvers are the ones which benefit the most. On the other hand, QMR ES witnesses only a marginal improvement, while QUANTOR and S K IZZO decrease, albeit not substantially, their performances. From these results, it seems that PREQUEL complements nicely variableelimination based solvers, while PROVERBOX suits more search-based ones. 0 improve the performances of all the solvers but S K IZZO, even if the improvement is more substantial with searchbased engines rather than variable-elimination ones.

On the other hand, QMR ES witnesses only a marginal improvement, while QUANTOR and S K IZZO decrease, albeit not substantially, their performances. From these results, it seems that PREQUEL complements nicely variableelimination based solvers, while PROVERBOX suits more search-based ones. 0 improve the performances of all the solvers but S K IZZO, even if the improvement is more substantial with searchbased engines rather than variable-elimination ones. 0 turns out to be the best choice overall.

In order to evaluate the impact of the gap between ˆ in practice, we consider the dataset of hard QBFs from the last QBF solvers ˆ p and tw tw competition (QBFEVAL’08). ˆ p (QBF – top) In Figure 1, for the above dataset, we present the distributions of tw ˆ (SAT – bottom). The distributions in Figure 1 are computed on 563 (out of 790) and tw 34 L. Pulina and A. Tacchella ˆ p (QBF - top) and tw ˆ (SAT - bottom). We Fig. 1. Treewidth of QBFEVAL’08 hard formulas: tw present distributions as box-and-whiskers plot representing the median (bold line), the first and third quartile (bottom and top edges of the box), the minimum and maximum (whiskers at the top and the bottom) of a distribution, on a logarithmic scale.

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