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This paper provides a formal and practical framework for sound abstraction ofprobabilistic actions. We start by precisely defining the concept of soundabstraction within the context of finite-horizon planning (where each plan is afinite sequence of actions). Next we show that such abstraction cannot beperformed within the traditional probabilistic action representation, whichmodels a world with a single probability distribution over the state space. Wethen present the constraint mass assignment representation, which models theworld with a set of probability distributions and is a generalization of massassignment representations. Within this framework, we present sound abstractionprocedures for three types of action abstraction. We end the paper withdiscussions and related work on sound and approximate abstraction. We givepointers to papers in which we discuss other sound abstraction-related issues,including applications, estimating loss due to abstraction, and automaticallygenerating abstraction hierarchies.

Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework