Multi-Agent-Based Simulation III: 4th International by Giovanna Di Marzo Serugendo (auth.), David Hales, Bruce

By Giovanna Di Marzo Serugendo (auth.), David Hales, Bruce Edmonds, Emma Norling, Juliette Rouchier (eds.)

This e-book constitutes the completely refereed post-proceedings of the 4th overseas Workshop on Multi-Agent-Based Simulation, MABS 2003, held in Melbourne, Australia as a part of AAMAS 2003.

The eleven revised complete papers offered including three invited papers have been conscientiously chosen in the course of rounds of reviewing and development. The papers are prepared in topical sections on MABS innovations for MAS; economics, trade, and effect in digital worlds; MABS strategies for real-world modelling, and knowing and classifying MABS.

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Extra resources for Multi-Agent-Based Simulation III: 4th International Workshop, MABS 2003, Melbourne, Australia, July 14, 2003. Revised Papers

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As the agents are autonomous, their behaviours are independent, which means that is not correlated to Thus, for each step of the simulation, there are four possible interaction situations which have to be handled by the interaction module. Interaction Management Modules. These modules, the core part of the experiment, are defined according to three different ways for managing the interactions between agents. The first technique is inspired by the method used in [11]: in this case, the agents’ actions are treated sequentially and each agent can reproduce, at his own turn, due to the proximity of a compatible partner.

The next section details the analysis of two experiments which are based on a minimalist CM. Section 4 discusses the results and proposes key concepts for refining a CM. The conclusion of this paper summarises the hypothesis and proposals of the paper. 1 The Engineering Divergence Phenomenon Principle of ABS Let us assume that defines the whole possible states of the studied system, every ABS is based on the assumption that the environment evolution from one moment to the next results from the composition of the actions, produced by the agents and of the environment’s action produced by its natural evolution, at In a simplified way, the problem is to build a time function, Dynamic such as: The symbol is used here to denote the action composition operator.

81, No. 5, pp. 1068–1094, 1991. 22. : “Perfect Equilibrium in a Bargaining Model,” Econometrica, Vol. 50, No. 1, pp. 97–109, 1982. 23. Smith, S. : “Flexible Learning of Problem Solving Heuristics through Adaptive Search,” The 8th International Joint Conference on Artificial Intelligence (IJCAI ’83), pp. 422–425, 1983. 24. : Bargaining Theory, Economics Research Institute at the Stockholm School of Economics, 1972. 25. Sutton, R. S. and Bart, A. : Reinforcement Learning – An Introduction –, The MIT Press, 1998.

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