@conference {1506763,
	title = {Planning with Continuous Resources for Agent Teams },
	booktitle = {The Eighth International Conference on Autonomous Agents and Multiagent Systems},
	year = {2009},
	abstract = {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 DecMDP approaches either discretize resources at the expense of speed
and quality guarantees, or avoid discretization only by limiting agents{\textquoteright}
action choices or interactions (e.g. assumption of transition independence). To address these shortcomings, this paper proposes MDPFP, 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.},
	author = {Janusz Marecki and Tambe, Milind}
}
