By Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)
This e-book constitutes the refereed lawsuits of the twenty fifth Canadian convention on synthetic Intelligence, Canadian AI 2012, held in Toronto, Canada, in might 2012. The 23 ordinary papers, sixteen brief papers, and four papers from the Graduate scholar Symposium offered have been rigorously reviewed and chosen for inclusion during this ebook. The papers hide a large variety of themes featuring unique paintings in all parts of synthetic intelligence, both theoretical or applied.
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Additional info for Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings
That is, their horizontal bars are not overlapping. In addition, to compare each pair of methods across multiple datasets, we show the win/lose/tie comparison record, as reported in Table 5 and Table 6 using MLP and SVM, respectively. Each record represents the number of datasets in which a method in the cloumn, respectively, wins over, loses to, or ties with the method of the corresponding row. To do that, we performed the non-parametric Wilcoxon signed rank test at 95% conﬁdence level. The results in Tables 2 and Table 3, along with the statistical tests presented in Table 4 and Table 5 and Fig.
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3 Experiment 2: Instance-Specific Macro Learning Using all benchmark domains, when possible, we conduct ﬁve sub-experiments: 1. 2. 3. 4. 5. FF and macros from Wizard’s chunking phase. FF and macros from Macro-FF CA-ED. FF and manually constructed macros. VHPOP and macros from Macro-FF CA-ED. VHPOP and manually constructed macros. Table 3. 1: Average run-times (in seconds) of the domain models constructed from chunking phase of Wizard using the FF planners. 05. 65 Macro Learning as Parameter Conﬁguration 21 Our interest here is to test whether our ParamILS-based instance-speciﬁc macro set (DIR-P) is as strong as the exhaustive instance-speciﬁc macro set (DIR), while requiring signiﬁcantly less oﬄine computation.