By Vinicius Oliverio, Estevam R. Hruschka Jr. (auth.), Juan Pavón, Néstor D. Duque-Méndez, Rubén Fuentes-Fernández (eds.)
This e-book constitutes the refereed lawsuits of the thirteenth Ibero-American convention on man made Intelligence, IBERAMIA 2012, held in Cartagena de Indias, Colombia, in November 2012. The seventy five papers awarded have been rigorously reviewed and chosen from one hundred seventy submissions. The papers are prepared in topical sections on wisdom illustration and reasoning, info and information processing, wisdom discovery and information mining, laptop studying, bio-inspired computing, fuzzy platforms, modelling and simulation, ambient intelligence, multi-agent structures, human-computer interplay, typical language processing, laptop imaginative and prescient and robotics, making plans and scheduling, AI in schooling, and information engineering and applications.
Read Online or Download Advances in Artificial Intelligence – IBERAMIA 2012: 13th Ibero-American Conference on AI, Cartagena de Indias, Colombia, November 13-16, 2012. Proceedings PDF
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Additional resources for Advances in Artificial Intelligence – IBERAMIA 2012: 13th Ibero-American Conference on AI, Cartagena de Indias, Colombia, November 13-16, 2012. Proceedings
Pav´ on et al. ): IBERAMIA 2012, LNAI 7637, pp. 21–30, 2012. L. Chac´ on and R. Pino P´erez Many merging operators have been deﬁned in that setting (a complete survey of logic-based merging is ). Most merging operators are based in semantical representations and the computational complexity of the entailment problem is at least in the second level of the polynomial hierarchy. Precisely, the problem of deciding if a formula is entailed by the revised base is in the class Π2P [9, 28] and the fact that belief revision operators are a particular case of belief merging operators  tells us that the last operators are complex at least concerning the entailment problem.
Mean Min. 64 Table 3. 1 Ladders-nets positive examples No-ladders-nets negative examples Total classification Ladders-net + Noladders-nets (Correct classification cases %) Mean Min. Max. Mean Min. Max. 44 Performance Comparison among Go Automated Players Hybrid player uses recognition patterns, strategic reasoning along with MCTS (MCRave) . Strategic player uses recognition patterns and strategic reasoning. MCRave player . 30 tests on board size of 9 x 9 of the following combination were performed to make a comparison among them; with this amount of simulations we obtain a certain degree of confidence but do not rule out a larger set.
Therefore, for each rule were inserted 20%, 30% and 50% of contradictions in the KB and then, RBCD was used to try to eliminate the artiﬁcially inserted mistakes. Figure 2 summarizes the obtained results. From the chart depicted in Fig. 2 is possible to see that when 50% of contradiction were inserted in the KB, RBCD was not able to solve any contradiction. It means that for a very noise KB (where 50% of the facts are wrong) RBCD can not ﬁnd any pattern among the instances and cannot help in eliminating contradictions.
Advances in Artificial Intelligence – IBERAMIA 2012: 13th Ibero-American Conference on AI, Cartagena de Indias, Colombia, November 13-16, 2012. Proceedings by Vinicius Oliverio, Estevam R. Hruschka Jr. (auth.), Juan Pavón, Néstor D. Duque-Méndez, Rubén Fuentes-Fernández (eds.)