Intractable problems in control theory
SIAM Journal on Control and Optimization
Multi-agent policies: from centralized ones to decentralized ones
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Multiagent planning for agents with internal execution resource constraints
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Minimizing communication cost in a distributed Bayesian network using a decentralized MDP
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Decentralized Markov Decision Processes with Event-Driven Interactions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Learning to Communicate and Act Using Hierarchical Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Analyzing Myopic Approaches for Multi-Agent Communication
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Reinforcement learning for DEC-MDPs with changing action sets and partially ordered dependencies
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Quantifying the Expected Utility of Information in Multi-agent Scheduling Tasks
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Introducing Communication in Dis-POMDPs with Locality of Interaction
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Network Distributed POMDP with Communication
New Frontiers in Artificial Intelligence
Agent influence as a predictor of difficulty for decentralized problem-solving
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A unification-based approach to configure generic protocols into agent interaction models
International Journal of Agent-Oriented Software Engineering
Measuring the expected gain of communicating constraint information
Multiagent and Grid Systems - Planning in multiagent systems
Introducing communication in Dis-POMDPs with locality of interaction
Web Intelligence and Agent Systems
Towards a unifying characterization for quantifying weak coupling in dec-POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Solving efficiently Decentralized MDPs with temporal and resource constraints
Autonomous Agents and Multi-Agent Systems
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The ability to coordinate effectively is critical for agents to accomplish their goals in a multi-agent system. A number of researchers have modeled the coordination problem for multi-agent systems using decision theory. The most general models have proven to be extremely complex to solve optimally (NEXP-complete). Some of the more restricted models have been much more tractable, though still difficult (NP-complete). What is missing is an understanding about why some models are much easier than others. This work fills this gap by providing a condition that distinguishes between problems in NP and those strictly harder than NP. This condition relates to the quantity of information each agent has about the others, and whether this information can be represented in a succinct way. We show that the class of problems that satisfy this condition is NP-complete. We illustrate this idea with two interaction protocols that satisfy the condition. For those problems that do not satisfy this condition we demonstrate how our theoretical results can be used to generate an NP approximation of the original problem.