By Joaquim Filipe, Ana Fred
This ebook constitutes the completely refereed post-conference court cases of the fifth overseas convention on brokers and synthetic Intelligence, ICAART 2013, held in Barcelona, Spain, in February 2013. The 20 revised complete papers awarded including one invited paper have been conscientiously reviewed and chosen from 269 submissions. The papers are prepared in topical sections on man made intelligence and on agents.
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Additional resources for Agents and Artificial Intelligence: 5th International Conference, ICAART 2013, Barcelona, Spain, February 15-18, 2013. Revised Selected Papers
Nested rollout policy adaptation for monte carlo tree search. In: Proceedings of the Twenty-Second International Joint Conference on Artiﬁcial Intelligence, IJCAI-2011, pp. 649–654 (2011) 40. : Montecarlo proof-number search for computer go. J. ) CG 2006. LNCS, vol. 4630, pp. 50–61. J. van den Herik et al. 41. : Single-player monte-carlo tree search. M. ) CG 2008. LNCS, vol. 5131, pp. 1–12. Springer, Heidelberg (2008) 42. : A minimax algorithm better than alpha-beta? Artif. Intell. 12(2), 179–196 (1979) 43.
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As in the previous 36 C. Hinrichs et al. section, a trade-oﬀ between run-time in terms of simulation steps, and run-time in terms of exchanged messages is visible. 5 yields the least messages on average during a whole simulation (chart not shown here). 4 Planning Horizon When the heuristic is applied to scheduling problems (as in the provisioning of active power, which we focus at), the dimensionality q of the target c ∈ IRq is interpreted as planning horizon. For real-world applications, it is interesting to know what planning horizon the heuristic is capable of.