Planning with continuous resources for agent teams

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.