Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
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
Dynamic programming for structured continuous Markov decision problems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Letting loose a SPIDER on a network of POMDPs: generating quality guaranteed policies
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Solving generalized semi-Markov decision processes using continuous phase-type distributions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
An iterative algorithm for solving constrained decentralized Markov decision processes
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Planning with continuous resources in stochastic domains
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Towards a unifying characterization for quantifying weak coupling in dec-POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Continuous time planning for multiagent teams with temporal constraints
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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Many problems of multiagent planning under uncertainty require distributed reasoning with continuous resources and resource limits. Decentralized Markov Decision Problems (Dec-MDPs) are well-suited to address such problems, but unfortunately, prior Dec-MDP approaches either discretize resources at the expense of speed and quality guarantees, or avoid discretization only by limiting agents' action choices or interactions (e.g. assumption of transition independence). To address these shortcomings, this paper proposes M-DPFP, a novel algorithm for planning with continuous resources for agent teams, with three key features: (i) it maintains the agent team interaction graph to identify and prune the suboptimal policies and to allow the agents to be transition dependent, (ii) it operates in a continuous space of probability functions to provide the error bound on the solution quality and finally (iii) it focuses the search for policies on the most relevant parts of this search space to allow for a systematic trade-off of solution quality for speed. Our experiments show that M-DPFP finds high quality solutions and exhibits superior performance when compared with a discretization-based approach. We also show that M-DPFP is applicable to solving problems that are beyond the scope of existing approaches.