By Evgeniy Gabrilovich (auth.), Yong Gao, Nathalie Japkowicz (eds.)
This booklet constitutes the refereed court cases of the 22st convention of the Canadian Society for Computational reports of Intelligence, Canadian AI 2009, held in Windsor, Canada, in could 2008.
The 30 revised complete papers offered including five revised brief papers and eight papers from the graduate pupil symposium have been conscientiously reviewed and chosen from seventy five submissions. The papers current unique fine quality learn in all components of synthetic Intelligence and observe old AI suggestions to fashionable challenge domain names in addition to fresh innovations to historic challenge settings.
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Extra info for Advances in Artificial Intelligence: 22nd Canadian Conference on Artificial Intelligence, Canadian AI 2009 Kelowna, Canada, May 25-27, 2009 Proceedings
Notice the logarithmic time scale. of literals), the SRSGI instances were more diﬃcult than the QGWH ones for all solvers. The comparison indicates that the hardness of SRSGI is comparable with that of QGWH instances, which are considered to be “hard”. Therefore we can say that the SRSGI instances are also hard. 4 Conclusion We investigated the empirical hardness of a generator of satisﬁable SAT instances, based on subgraph isomorphism. We noticed an easy-hard-easy pattern of the evolution of the empirical hardness of the instances, which allows for a ﬁne tuning of the hardness.
The correlation is almost linear but this may be a consequence of the sample size. The positive correlation between n and p is an immediate consequence of the two inverse correlations presented above. In table 1 we present, for each value of m, the values of n, q and p which produced the hardest instances3 . It appears that the hardest instances are generated when n=m-2. 3 Growth Rate of the Hardest Instances The Subgraph Isomorphism Problem is NP-complete, and thus it is assumed that it is exponentially hard for deterministic algorithms.
This is several orders of magnitude smaller than the experiments we conducted in this study. There are several issues we are currently investigating. First, we only considered simple word-alignment models in this study. Higher-level IBM models can potentially improve the quality of the word alignments produced. At the very least, HMM models , for which Viterbi alignments can be computed eﬃciently, should be considered. The alignment method used in current phrasebased SMT is another alternative we are considering.
Advances in Artificial Intelligence: 22nd Canadian Conference on Artificial Intelligence, Canadian AI 2009 Kelowna, Canada, May 25-27, 2009 Proceedings by Evgeniy Gabrilovich (auth.), Yong Gao, Nathalie Japkowicz (eds.)