By Katia Sycara (auth.), F. M. T. Brazier, Kees Nieuwenhuis, Gregor Pavlin, Martijn Warnier, Costin Badica (eds.)
This publication represents the mixed peer-reviewed court cases of the 5th foreign Symposium on clever disbursed Computing -- IDC 2011 and of the 3rd overseas Workshop on Multi-Agent structures expertise and Semantics -- MASTS 2011. either occasions have been held in Delft, The Netherlands in the course of October 5-7, 2011.
The 33 contributions released during this ebook handle many subject matters relating to concept and purposes of clever disbursed computing and multi-agent structures, together with: adaptive and self sustaining dispensed structures, agent programming, ambient assisted dwelling platforms, enterprise approach modeling and verification, cloud computing, coalition formation, choice help platforms, allotted optimization and constraint delight, gesture popularity, clever power administration in WSNs, clever logistics, laptop studying, cellular brokers, parallel and disbursed computational intelligence, parallel evolutionary computing, belief metrics and protection, scheduling in disbursed heterogenous computing environments, semantic internet carrier composition, social simulation, and software program brokers for WSNs.
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Extra info for Intelligent Distributed Computing V: Proceedings of the 5th International Symposium on Intelligent Distributed Computing – IDC 2011, Delft, The Netherlands – October 2011
600 500 400 300 SWF 200 MaxSum 100 Fig. 6 Histogram of the number of Packages per Agent 0 1 2 3 4 5 6 7 8 9 10 11 SOCIAL DCOP - Social Choice in Distributed Constraints Optimization 45 The use of MaxSum as the objective function results in some agents picking up a large number of packages. Since the solution complexity of the PDP in the general case is exponential in the number of packages, solving the problem with MaxSum as the target function results in a much higher run time than using the Theil SWF as a target function.
In the following table the Leuven and Brussels scenarios are characterized. 22, based on the difference in distance between package origin and destination as shown in the table above. Experiment results. For both experiments the same two dependent variables are measured: lateness distance The average lateness of the delivery of all packages (in minutes). The average distance traveled by all trucks (in kilometer). Figure 4(a) shows lateness of both the fast adapting and the slow adapting solution.
Agent decision logic. As described earlier, truck agents make decisions about path selection by using heuristics. This decision determines the order at which packages should be serviced. In Figure 3 a heuristic and three helper functions are introduced. The implementation conforms to the heuristic objectives and is used in the experiments described in section 4. The pseudocode first considers time window violations, then lateness followed by distance traveled. In order to deal with dynamic environments, ants are sent out periodically.