By Sebastian Ahrndt, Johannes Fähndrich, Sahin Albayrak (auth.), Yves Demazeau, Toru Ishida, Juan M. Corchado, Javier Bajo (eds.)
This publication constitutes the refereed complaints of the eleventh overseas convention on useful functions of brokers and Multi-Agent platforms, PAAMS 2013, held in Salamanca, Spain, in may perhaps 2013. The 14 revised complete papers and nine brief papers offered including sixteen demonstrations have been conscientiously reviewed and chosen from 70 submissions. The papers record at the software and validation of agent-based versions, tools, and applied sciences in a few key software parts, together with: brokers for actual global difficulties; crowd modeling and research; choice making and discovery; interplay with synthetic brokers; mobility, ubiquity and clouds; (multi-)agent layout know-how; and simulation and organization.
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Extra resources for Advances on Practical Applications of Agents and Multi-Agent Systems: 11th International Conference, PAAMS 2013, Salamanca, Spain, May 22-24, 2013. Proceedings
US Energy Information: Annual Energy Review 2010, US Government Printing Oﬃce (October 2011) 15. : Agent-based simulation of electricity markets: a survey of tools. it Abstract. This paper presents MAKKSim, a pedestrian dynamics simulator based on a computational discrete model in which pedestrians are represented as utility-based agents. The computational model and the system architecture are discussed, focusing on the development of the tool and on its application in a real-word case study, for the comparison and the evaluation of diﬀerent strategies of crowd management and of diﬀerent structural changes on the geometry of the environment.
This grid is obtained as follows: agents store the value of the density ﬁeld in its position in the grid and at the end of the simulation the mean value is then calculated. Note that, with this method, CMD values of empty cells gets no variation, therefore the grid does not vary on time; • PedsNumberStat saves the number of agents in the simulation; • PedsActionsStat stores every action performed by the agents during all the simulation, to analyse their trajectories; • PedsTravellingTimeStat stores the number of steps required by agents to reach their goal; • GroupsAreaStat stores the values of pedestrian groups dispersion during the simulation.
Smart Grid, 109–119 (2010) 7. : Using design of experiments methods to develop low energy building model under modelica. In: IBPSA 2011 (2012) 8. , Jennings, N. : Adaptive home heating control through gaussian process prediction and mathematical programming. In: Second Int. Workshop on Agent Technology for Energy Systems (ATES 2011), pp. 71–78 (May 2011) 9. : Speech Acts. Cambridge University Press (1969) 10. : Incremental and situated modeling for multi-agent based simulations. In: Proc. 2010 IEEE RIVF Conference, pp.
Advances on Practical Applications of Agents and Multi-Agent Systems: 11th International Conference, PAAMS 2013, Salamanca, Spain, May 22-24, 2013. Proceedings by Sebastian Ahrndt, Johannes Fähndrich, Sahin Albayrak (auth.), Yves Demazeau, Toru Ishida, Juan M. Corchado, Javier Bajo (eds.)