Cooperative games with stochastic payoffs

Suijs, Jeroen; Borm, Peter; Waegenaere, Anja De; Tijs, S.H.

doi:10.1016/s0377-2217(97)00421-9

Cited by 125 publications

(87 citation statements)

References 9 publications

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“…Suijs et al [57,56] introduce stochastic cooperative games (SCGs), comprising a set of agents, a set of coalitional actions, and a function assigning to each action a random variable with finite expectation, representing the payoff to the coalition when this action is taken. Thus uncertainty in coalitional value is present.…”

Section: Definitionmentioning

confidence: 99%

“…Also, no assump-tion of incomplete information about partners's types is made, and thus there is no direct translation of type uncertainty into coalition value uncertainty. However, [57] discusses the effect that different risk behaviour on the part of agents might have on the existence of a core allocation within a specific class of SCG games.…”

Section: Definitionmentioning

confidence: 99%

“…Because of the stochastic nature of payoffs in BCFPs, we assume that agents join a coalition with certain relative payoff demands [57,56]. Intuitively, since action uncertainty means agents cannot predict coalition payoff (and, consequently, the payoff shares to coalition members) with certainty, it is natural to place relative demands on the fractional share of the realized payoff.…”

Section: Definition 2 (Bayesian Coalition Formation Problem [12 15])mentioning

confidence: 99%

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Sequentially optimal repeated coalition formation under uncertainty

Chalkiadakis

Boutilier

2010

Auton Agent Multi-Agent Syst

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Coalition formation is a central problem in multiagent systems research, but most models assume common knowledge of agent types. In practice, however, agents are often unsure of the types or capabilities of their potential partners, but gain information about these capabilities through repeated interaction. In this paper, we propose a novel Bayesian, model-based reinforcement learning framework for this problem, assuming that coalitions are formed (and tasks undertaken) repeatedly. Our model allows agents to refine their beliefs about the types of others as they interact within a coalition. The model also allows agents to make explicit tradeoffs between exploration (forming "new" coalitions to learn more about the types of new potential partners) and exploitation (relying on partners about which more is known), using value of information to define optimal exploration policies. Our framework effectively integrates decision making during repeated coalition formation under type uncertainty with Bayesian reinforcement learning techniques. Specifically, we present several learning algorithms to approximate the optimal Bayesian solution to the repeated coalition formation and type-learning problem, providing tractable means to ensure good sequential performance. We evaluate our algorithms in a variety of settings, showing that one method in particular exhibits consistently good performance in practice. We also demonstrate the ability of our model to facilitate knowledge transfer across different dynamic tasks.

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Section: Definitionmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Section: Definition 2 (Bayesian Coalition Formation Problem [12 15])mentioning

confidence: 99%

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Sequentially optimal repeated coalition formation under uncertainty

Chalkiadakis

Boutilier

2010

Auton Agent Multi-Agent Syst

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“…Work on coalitional games under uncertainty includes Suijs et al [1999], Chalkiadakis and , Myerson [2007], and Ieong and Shoham [2008]; as mentioned earlier, many open problems remain.…”

Section: History and Referencesmentioning

confidence: 99%

Multiagent systems: algorithmic, game-theoretic, and logical foundations

Shoham

Leyton‐Brown

2009

Choice Reviews Online

299

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“…A game with stochastic payoffs 17 is defined by a set of agents, a set of possible actions coalitions might take, and a function assigning to each action of a coalition a real-valued stochastic variable with finite expectation, representing the payoff to a coalition when this particular action is taken. Thus, in contrast to a deterministic cooperative game, the payoffs can be random variables, and the actions a coalition can choose from are explicitly modeled, because the payoffs are not uniquely determined.…”

Section: Stochastic Cooperative Gamesmentioning

confidence: 99%

Dynamic coalition formation among rational agents

2002

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Designing self-interested autonomous software agents that can negotiate rationally in stable coalitions can dramatically benefit end users. Rational agents are usually required to form beneficial coalitions in open, distributed, and heterogeneous environments, including scenarios in which dynamically occurring events might interfere with the coalition proceses. Dynamic coalition formation (DCF) methods promise to be particularly well suited for applications of ubiquitous and mobile computing, including mobile commerce in wireless environments.In mobile-commerce settings, for example, personalized information agents, each representing a potential business partner, might dynamically form temporary profit-oriented coalitions to enhance a customer's purchasing and negotiating strategies in multiple electronic marketplaces. This vision for the common Internet user isn't far from being realized, especially with recent advances in wireless computing and communication appliances. [1][2][3]

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Cooperative games with stochastic payoffs

Cited by 125 publications

References 9 publications

Sequentially optimal repeated coalition formation under uncertainty

Sequentially optimal repeated coalition formation under uncertainty

Multiagent systems: algorithmic, game-theoretic, and logical foundations

Dynamic coalition formation among rational agents

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