Multi-agent policies: from centralized ones to decentralized ones
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
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
Reasoning about joint beliefs for execution-time communication decisions
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Formal models and algorithms for decentralized decision making under uncertainty
Autonomous Agents and Multi-Agent Systems
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
The complexity of decentralized control of Markov decision processes
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Learning collaborative team behavior from observation
Expert Systems with Applications: An International Journal
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Despite their worst-case NEXP-complete planning complexity, DEC-POMDPs remain a popular framework for multiagent teamwork. This paper introduces effective teamwork under model uncertainty (i.e., potentially inaccurate transition and observation functions) as a novel challenge for DEC-POMDPs and presents MODERN, the first execution-centric framework for DEC-POMDPs explicitly motivated by addressing such model uncertainty. MODERN's shift of coordination reasoning from planning-time to execution-time avoids the high cost of computing optimal plans whose promised quality may not be realized in practice. There are three key ideas in MODERN: (i) it maintains an exponentially smaller model of other agents' beliefs and actions than in previous work and then further reduces the computation-time and space expense of this model via bounded pruning, (ii) it reduces execution-time computation by exploiting BDI theories of teamwork, and limits communication to key trigger points, and (iii) it limits its decision-theoretic reasoning about communication to trigger points and uses a systematic markup to encourage extra communication at these points--thus reducing uncertainty among team members at trigger points. We empirically show that MODERN is substantially faster than existing DEC-POMDP execution-centric methods while achieving significantly higher reward.