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Chapitre D'ouvrage Année : 2023

A solution concept with an exploration bias for repeated stochastic coalitional games

Résumé

Classically, in coalition formation, agents know in advance the deterministic utilities they will obtain from coalitions. Relaxing these two assumptions (determinism and a priori knowledge) is important to deal with real-world applications. A way to do that is to consider the framework of repeated stochastic coalitional games. Here, agents decide at each time step which coalition to form on the basis of limited information. Then, their observations allow them to update their knowledge. We propose a solution concept that explicitly integrates an exploration bias to allow agents to sometimes form coalitions that have a low utility but that would be interesting to form to obtain more information. We compare this concept to a greedy approach and highlight its efficiency with respect to the structure of the real utilities, unknown to the agents.
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Dates et versions

hal-04389964 , version 1 (12-01-2024)

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Josselin Guéneron, Grégory Bonnet. A solution concept with an exploration bias for repeated stochastic coalitional games. Philippe Mathieu; Frank Dignum; Paulo Novais; Fernando De la Prieta. Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection 21st International Conference, PAAMS 2023, Guimarães, Portugal, July 12–14, 2023, Proceedings, 13955, Springer Nature Switzerland, pp.100-112, 2023, Lecture Notes in Computer Science, 978-3-031-37615-3. ⟨10.1007/978-3-031-37616-0_9⟩. ⟨hal-04389964⟩
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