17,901 research outputs found
Computational Approaches for Stochastic Shortest Path on Succinct MDPs
We consider the stochastic shortest path (SSP) problem for succinct Markov
decision processes (MDPs), where the MDP consists of a set of variables, and a
set of nondeterministic rules that update the variables. First, we show that
several examples from the AI literature can be modeled as succinct MDPs. Then
we present computational approaches for upper and lower bounds for the SSP
problem: (a)~for computing upper bounds, our method is polynomial-time in the
implicit description of the MDP; (b)~for lower bounds, we present a
polynomial-time (in the size of the implicit description) reduction to
quadratic programming. Our approach is applicable even to infinite-state MDPs.
Finally, we present experimental results to demonstrate the effectiveness of
our approach on several classical examples from the AI literature
Hierarchical Decomposition and Analysis for Generalized Planning
This paper presents new methods for analyzing and evaluating generalized
plans that can solve broad classes of related planning problems. Although
synthesis and learning of generalized plans has been a longstanding goal in AI,
it remains challenging due to fundamental gaps in methods for analyzing the
scope and utility of a given generalized plan. This paper addresses these gaps
by developing a new conceptual framework along with proof techniques and
algorithmic processes for assessing termination and goal-reachability related
properties of generalized plans. We build upon classic results from graph
theory to decompose generalized plans into smaller components that are then
used to derive hierarchical termination arguments. These methods can be used to
determine the utility of a given generalized plan, as well as to guide the
synthesis and learning processes for generalized plans. We present theoretical
as well as empirical results illustrating the scope of this new approach. Our
analysis shows that this approach significantly extends the class of
generalized plans that can be assessed automatically, thereby reducing barriers
in the synthesis and learning of reliable generalized plans.Comment: Accepted for publication at JAI
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