Communication decisions in multi-agent cooperation: model and experiments
Proceedings of the fifth international conference on Autonomous agents
Transition-independent decentralized markov decision processes
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Optimizing information exchange in cooperative multi-agent systems
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
Decentralized planning under uncertainty for teams of communicating agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning to communicate in a decentralized environment
Autonomous Agents and Multi-Agent Systems
Exploiting factored representations for decentralized execution in multiagent teams
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Exploiting locality of interaction in factored Dec-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Interaction-driven Markov games for decentralized multiagent planning under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Formal models and algorithms for decentralized decision making under uncertainty
Autonomous Agents and Multi-Agent Systems
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
On the difficulty of achieving equilibrium in interactive POMDPs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Agent influence as a predictor of difficulty for decentralized problem-solving
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
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Communication-based decomposition mechanisms for decentralized MDPs
Journal of Artificial Intelligence Research
Optimal and approximate Q-value functions for decentralized POMDPs
Journal of Artificial Intelligence Research
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Offline Planning for Communication by Exploiting Structured Interactions in Decentralized MDPs
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Decentralized MDPs with sparse interactions
Artificial Intelligence
Scaling up optimal heuristic search in Dec-POMDPs via incremental expansion
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) provide powerful modeling tools for multiagent decision-making in the face of uncertainty, but solving these models comes at a very high computational cost. Two avenues for side-stepping the computational burden can be identified: structured interactions between agents and intra-agent communication. In this paper, we focus on the interplay between these concepts, namely how sparse interactions impact the communication needs. A key insight is that in domains with local interactions the amount of communication necessary for successful joint behavior can be heavily reduced, due to the limited influence between agents. We exploit this insight by deriving local POMDP models that optimize each agent's communication behavior. Our experimental results show that our approach successfully exploits sparse interactions: we can effectively identify the situations in which it is beneficial to communicate, as well as trade off the cost of communication with overall task performance.