Communication decisions in multi-agent cooperation: model and experiments
Proceedings of the fifth international conference on Autonomous agents
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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
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
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Online planning for multi-agent systems with bounded communication
Artificial Intelligence
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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In decentralized settings with partial observability, agents can often benefit from communicating, but communication resources may be limited and costly. Current approaches tend to dismiss or underestimate this cost, resulting in over-communication. This paper presents a general framework to compute the value of communicating from each agent’s local perspective, by comparing the expected reward with and without communication. In order to obtain these expectations, each agent must reason about the state and belief states of the other agents, both before and after communication. We show how this can be done in the context of decentralized POMDPs and discuss ways to mitigate a common myopic assumption, where agents tend to over-communicate because they overlook the possibility that communication can be deferred or initiated by the other agents. The paper presents a theoretical framework to precisely quantify the value of communication and an effective algorithm to manage communication. Experimental results show that our approach performs well compared to other techniques suggested in the literature.