By Joaquim Filipe, Ana Fred
This e-book constitutes the completely refereed post-conference lawsuits of the fifth overseas convention on brokers and synthetic Intelligence, ICAART 2013, held in Barcelona, Spain, in February 2013. The 20 revised complete papers provided including one invited paper have been rigorously reviewed and chosen from 269 submissions. The papers are prepared in topical sections on man made intelligence and on agents.
Read or Download Agents and Artificial Intelligence: 5th International Conference, ICAART 2013, Barcelona, Spain, February 15-18, 2013. Revised Selected Papers PDF
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Extra resources for Agents and Artificial Intelligence: 5th International Conference, ICAART 2013, Barcelona, Spain, February 15-18, 2013. Revised Selected Papers
7 or if one cannot run with a suﬃcient number of expansions to make use of its properties. 1 The results suggest that if the number of variables is in the range of 13 to 15 an appropriate value for the number of expansions is 200–250. This number will then be multiplied by the number of runs of MCTS to obtain an indication for the total number of tree expansions. For reasons of comparison, we remark that similar studies of other physics formulas with more variables (O(30)) show larger optimal values for the number of expansions per run and less pronounced local minima.
Int. J. Autonomic Comput. 1(4), 350 (2010) 21. : Self-organization in communication networks: principles and design paradigms. IEEE Commun. Mag. 43(7), 78–85 (2005). arnumber=1470824 22. : Putting the“Smarts” into the smart grid: A grand challenge for artiﬁcial intelligence. Commun. ACM 55(4), 86 (2012) 23. : Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Comput. Graph. 21(4), 25–34 (1987) 24. : Self-organisation in multi-agent systems. Knowl. Eng. Rev. 20(2), 65–189 (2005) 25.
The agent publishes Σi∗ to its neighbors, who accept it as a new best conﬁguration, and re-publish it again to their respective neighbors. Hence, the distribution of Σ ∗ (shaded area) rises in the following time steps, but the ﬁtness value (solid line) remains constant. In simulation step 30, however, some agent ak ﬁnds an even better conﬁguration Σk∗ . Thus the ﬁtness value improves and, from this point in time, Σ ∗ = Σk∗ . At the same time, the distribution of the (new) Σ ∗ drops dramatically, since this conﬁguration is known to ak only and has yet to be spread in the network.
Agents and Artificial Intelligence: 5th International Conference, ICAART 2013, Barcelona, Spain, February 15-18, 2013. Revised Selected Papers by Joaquim Filipe, Ana Fred