The complexity of Markov decision processes
Mathematics of Operations Research
Collaborative plans for complex group action
Artificial Intelligence
Distributed problem solving and planning
Multiagent systems
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AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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Reflections on the Nature of Multi-Agent Coordination and Its Implications for an Agent Architecture
Autonomous Agents and Multi-Agent Systems
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
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Introducing Communication in Dis-POMDPs with Locality of Interaction
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Towards Addressing Model Uncertainty: Robust Execution-Time Coordination for Teamwork
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Coordinating teams in uncertain environments: a hybrid BDI-POMDP approach
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Online planning for ad hoc autonomous agent teams
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Decentralised channel allocation and information sharing for teams of cooperative agents
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Decentralized Bayesian reinforcement learning for online agent collaboration
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Solving decentralized POMDP problems using genetic algorithms
Autonomous Agents and Multi-Agent Systems
Bayesian interaction shaping: learning to influence strategic interactions in mixed robotic domains
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Distributed reasoning for multiagent simple temporal problems
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Agent-based decentralised coordination for sensor networks using the max-sum algorithm
Autonomous Agents and Multi-Agent Systems
Multiagent meta-level control for radar coordination
Web Intelligence and Agent Systems
Map partitioning to approximate an exploration strategy in mobile robotics
Multiagent and Grid Systems
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Planning for distributed agents with partial state information is considered from a decisiontheoretic perspective. We describe generalizations of both the MDP and POMDP models that allow for decentralized control. For even a small number of agents, the finite-horizon problems corresponding to both of our models are complete for nondeterministic exponential time. These complexity results illustrate a fundamental difference between centralized and decentralized control of Markov processes. In contrast to the MDP and POMDP problems, the problems we consider provably do not admit polynomialtime algorithms and most likely require doubly exponential time to solve in the worst case. We have thus provided mathematical evidence corresponding to the intuition that decentralized planning problems cannot easily be reduced to centralized problems and solved exactly using established techniques.