Multi-agent policies: from centralized ones to decentralized ones
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
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
A Robust Primal-Dual Interior-Point Algorithm for Nonlinear Programs
SIAM Journal on Optimization
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Conflicts in teamwork: hybrids to the rescue
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Security in multiagent systems by policy randomization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Coordinating randomized policies for increasing security of agent systems
Information Technology and Management
Sensitivity analysis for distributed optimization with resource constraints
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Despite the recent advances in distributed MDP frameworks for reasoning about multiagent teams, these frameworks mostly do not reason about resource constraints, a crucial issue in teams. To address this shortcoming, we provide four key contributions. First, we introduce EMTDP, a distributed MDP framework where agents must not only maximize expected team reward, but must simultaneously bound expected resource consumption. While there exist single-agent constrained MDP (CMDP) frameworks that reason about resource constraints, EMTDP is not just a CMDP with multiple agents. Instead, EMTDP must resolve the miscoordination that arises due to policy randomization. Thus, our second contribution is an algorithm for EMTDP transformation, so that resulting policies, even if randomized, avoid such miscoordination. Third, we prove equivalence of different techniques of EMTDP transformation. Finally, we present solution algorithms for these EMTDPs and show through experiments their efficiency in solving application-sized problems.