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AAAI
2008

Towards Faster Planning with Continuous Resources in Stochastic Domains

8 years 9 months ago
Towards Faster Planning with Continuous Resources in Stochastic Domains
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's key contribution is its exploitation of the dual space cumulative distribution functions. This dual formulation is key to DPFP's novel combination of three features. First, it enables DPFP'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 appro...
Janusz Marecki, Milind Tambe
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where AAAI
Authors Janusz Marecki, Milind Tambe
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