Abstract
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.
Keywords
- Nash Equilibrium
- Multiagent System
- Dynamic Game
- Stochastic Game
- Iteration Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2005 Springer-Verlag Berlin Heidelberg
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Camponogara, E., Zhou, H. (2005). Seeking Multiobjective Optimization in Uncertain, Dynamic Games. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_56
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DOI: https://doi.org/10.1007/11595014_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
Online ISBN: 978-3-540-31646-6
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