On the complexity of cooperative solution concepts
Mathematics of Operations Research
Coalitions among computationally bounded agents
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Bayesian Reinforcement Learning for Coalition Formation under Uncertainty
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Coalition formation under uncertainty: bargaining equilibria and the Bayesian core stability concept
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Coalitional bargaining with agent type uncertainty
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On the computational complexity of coalitional resource games
Artificial Intelligence
Coalition structure generation utilizing compact characteristic function representations
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
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The study of cooperation among agents is of central interest in multiagent systems research. A popular way to model cooperation is through coalitional game theory. Much research in this area has had limited practical applicability as regards real-world multi-agent systems due to the fact that it assumes deterministic payoffs to coalitions and in addition does not apply to multi-agent environments that are stochastic in nature. In this paper, we propose a novel approach to modeling such scenarios where coalitional games will be contextualized through the use of logical expressions representing environmental and other state, and probability distributions will be placed on the space of contexts in order to model the stochastic nature of the scenarios. More formally, we present a formal representation language for representing contextualized coalitional games embedded in stochastic environments and we define and show how to compute expected Shapley values in such games in a computationally efficient manner. We present the value of the approach through an example involving robotics assistance in emergencies.