Multiagent System Technologies: 11th German Conference, by Catholijn Jonker (auth.), Matthias Klusch, Matthias Thimm,

By Catholijn Jonker (auth.), Matthias Klusch, Matthias Thimm, Marcin Paprzycki (eds.)

This booklet constitutes the refereed court cases of the eleventh German convention on Multiagent procedure applied sciences, associates 2013, held in Koblenz, Germany, in September 2013. The 29 revised complete papers and three keynote talks awarded have been conscientiously reviewed and chosen from a number of submissions. The papers hide a large sector of subject matters of curiosity starting from problems with agent-based coordination to simulation to negotiation.

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Additional resources for Multiagent System Technologies: 11th German Conference, MATES 2013, Koblenz, Germany, September 16-20, 2013. Proceedings

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8. Results Plain, 1000 × 1000 cells All figures from Figure 4 to Figure 8 show, that the run time of the Multi-Agent Flood algorithm is a little bit slower than the Brick and Mortar algorithm. The diagrams depicting the results of the bigger test cases (Fig. 7 and 8) show, that this difference is smaller in the larger scenarios, especially in Fig. 7, where the MAF algorithm is faster than the BM algorithm. This is particularly interesting, as the movement routine of the MAF agents is still unoptimized and each agent will return to the base to report the location of the latest point of interest, that was found by said agent.

The security model presented here aims at open distributed systems, in which agents are used to realize some of the systems functionality. g. may be accessed using message-based interaction protocols. It is assumed that agent platforms represent the nodes in the network and each agent platform is under some administrative control. The focus is on security issues due to agent services being accessed from the outside (cf. Fig. 1). With regard to the usage of these services four common security objectives can be identified.

Enabling the exchange of data about already explored terrain and already uncovered paths back to the starting point. The “Return Mode” can also be optimized to yield shorter paths on the return to the base. References 1. : Dynamic Programming. Princeton University Press, Princeton (1972) 2. : The IUB Rugbot: an intelligent, rugged mobile robot for search and rescue operations. In: IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR). IEEE Press (2006) 3. : Ant colony system: A cooperative learning approach to the traveling salesman problem.

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