Approximate Solution Techniques for Factored First-Order MDPs

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Approximate Solution Techniques for Factored First-Order MDPs
Most traditional approaches to probabilistic planning in relationally specified MDPs rely on grounding the problem w.r.t. specific domain instantiations, thereby incurring a combinatorial blowup in the representation. An alternative approach is to lift a relational MDP to a firstorder MDP (FOMDP) specification and develop solution approaches that avoid grounding. Unfortunately, state-of-the-art FOMDPs are inadequate for specifying factored transition models or additive rewards that scale with the domain size—structure that is very natural in probabilistic planning problems. To remedy these deficiencies, we propose an extension of the FOMDP formalism known as a factored FOMDP and present generalizations of symbolic dynamic programming and linear-value approximation solutions to exploit its structure. Along the way, we also make contributions to the field of first-order probabilistic inference (FOPI) by demonstrating novel first-order structures that can be exploited without d...
Scott Sanner, Craig Boutilier
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2007
Where AIPS
Authors Scott Sanner, Craig Boutilier
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