Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Computation and action under bounded resources
Computation and action under bounded resources
On the complexity of cooperative solution concepts
Mathematics of Operations Research
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
Coalitions among computationally bounded agents
Artificial Intelligence - Special issue on economic principles of multi-agent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Coalition structure generation with worst case guarantees
Artificial Intelligence
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dynamic Coalition Formation among Rational Agents
IEEE Intelligent Systems
Multi-Agent Coordination through Coalition Formation
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Coalition formation with uncertain heterogeneous information
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Fuzzy kernel-stable coalitions between rational agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Coordination in multiagent reinforcement learning: a Bayesian approach
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Optimal learning: computational procedures for bayes-adaptive markov decision processes
Optimal learning: computational procedures for bayes-adaptive markov decision processes
Accelerating reinforcement learning through imitation
Accelerating reinforcement learning through imitation
Modelling Coalition Formation over Time for Iterative Coalition Games
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
The Advantages of Compromising in Coalition Formation with Incomplete Information
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Adaptive, Confidence-Based Multiagent Negotiation Strategy
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
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
Organization-Based Coalition Formation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Trusted kernel-based coalition formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
BSCA-F: E.cient Fuzzy Valued Stable Coalition Forming Among Agents
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Agent-based virtual organisations for the Grid
Multiagent and Grid Systems - Smart Grid Technologies & Market Models
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
Sequential decision making with untrustworthy service providers
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Sequential decision making in repeated coalition formation under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
A bayesian approach to multiagent reinforcement learning and coalition formation under uncertainty
A bayesian approach to multiagent reinforcement learning and coalition formation under uncertainty
Methods for empirical game-theoretic analysis
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Coalitional bargaining with agent type uncertainty
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Complexity of determining nonemptiness of the core
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Methods for task allocation via agent coalition formation
Artificial Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence
Communications of the ACM
Decentralized Bayesian reinforcement learning for online agent collaboration
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Agent-based game-theoretic model for collaborative web services: Decision making analysis
Expert Systems with Applications: An International Journal
Coalition formation based on marginal contributions and the Markov process
Decision Support Systems
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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.