The complexity of Markov decision processes
Mathematics of Operations Research
General principles of learning-based multi-agent systems
Proceedings of the third annual conference on Autonomous Agents
Distributed Anonymous Mobile Robots: Formation of Geometric Patterns
SIAM Journal on Computing
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Communication decisions in multi-agent cooperation: model and experiments
Proceedings of the fifth international conference on Autonomous agents
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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
Markov Decision Processes with Observation Costs TITLE2:
Markov Decision Processes with Observation Costs TITLE2:
Communication for Improving Policy Computation in Distributed POMDPs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
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
Formal models and algorithms for decentralized decision making under uncertainty
Autonomous Agents and Multi-Agent Systems
Anytime coordination using separable bilinear programs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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
Memory-bounded dynamic programming for DEC-POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Distributed planning in hierarchical factored MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A bilinear programming approach for multiagent planning
Journal of Artificial Intelligence Research
QueryPOMDP: POMDP-based communication in multiagent systems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Modeling information exchange opportunities for effective human-computer teamwork
Artificial Intelligence
Organizational design principles and techniques for decision-theoretic agents
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Modeling human behavior in the aftermath of a hypothetical improvised nuclear detonation
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
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Multi-agent planning in stochastic environments can be framed formally as a decentralized Markov decision problem. Many real-life distributed problems that arise in manufacturing, multi-robot coordination and information gathering scenarios can be formalized using this framework. However, finding the optimal solution in the general case is hard, limiting the applicability of recently developed algorithms. This paper provides a practical approach for solving decentralized control problems when communication among the decision makers is possible, but costly. We develop the notion of communication-based mechanism that allows us to decompose a decentralized MDP into multiple single-agent problems. In this framework, referred to as decentralized semi-Markov decision process with direct communication (Dec-SMDP-Com), agents operate separately between communications. We show that finding an optimal mechanism is equivalent to solving optimally a Dec-SMDP-Com. We also provide a heuristic search algorithm that converges on the optimal decomposition. Restricting the decomposition to some specific types of local behaviors reduces significantly the complexity of planning. In particular, we present a polynomial-time algorithm for the case in which individual agents perform goal-oriented behaviors between communications. The paper concludes with an additional tractable algorithm that enables the introduction of human knowledge, thereby reducing the overall problem to finding the best time to communicate. Empirical results show that these approaches provide good approximate solutions.