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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Seeking Multiobjective Optimization in Uncertain, Dynamic Games
verfasst von : Eduardo Camponogara, Haoyu Zhou
Erschienen in: Progress in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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If the decisions of agents arise from the solution of general unconstrained problems, altruistic agents can implement effective problem transformations to promote convergence to attractors and draw these fixed points toward Pareto optimal points. In the literature, algorithms have been developed to compute optimal parameters for problem transformations in the seemingly more restrictive scenario of uncertain, quadratic games in which an agent’s response is induced by one of a set of potential problems. This paper reviews these developments briefly and proposes a convergent algorithm that enables altruistic agents to relocate the attractor at a point at which all agents are better off, rather than optimizing a weighted function of the agents’ objectives.