@conference {1508551,
	title = {Towards Faster Planning with Continuous Resources in Stochastic Domains },
	booktitle = {Twenty Third AAAI Conference on Artificial Intelligence	},
	year = {2008},
	abstract = {Agents often have to construct plans that obey resource limits for continuous resources whose consumption can only be
characterized by probability distributions. While Markov Decision Processes (MDPs) with a state space of continuous and
discrete variables are popular for modeling these domains,
current algorithms for such MDPs can exhibit poor performance with a scale-up in their state space. To remedy that
we propose an algorithm called DPFP. DPFP{\textquoteright}s key contribution is its exploitation of the dual space cumulative distribution functions. This dual formulation is key to DPFP{\textquoteright}s novel
combination of three features. First, it enables DPFP{\textquoteright}s membership in a class of algorithms that perform forward search
in a large (possibly infinite) policy space. Second, it provides
a new and efficient approach for varying the policy generation effort based on the likelihood of reaching different regions of the MDP state space. Third, it yields a bound on
the error produced by such approximations. These three features conspire to allow DPFP{\textquoteright}s superior performance and systematic trade-off of optimality for speed. Our experimental
evaluation shows that, when run stand-alone, DPFP outperforms other algorithms in terms of its any-time performance,
whereas when run as a hybrid, it allows for a significant
speedup of a leading continuous resource MDP solver.},
	author = {Janusz Marecki and Tambe, Milind}
}
