Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework
This work addresses a foundational issue in AI planning for scenarios requiring probabilistic reasoning, though it appears incremental as it builds on existing mass assignment representations.
The paper tackles the problem of sound abstraction for probabilistic actions in finite-horizon planning by introducing the constraint mass assignment representation, which generalizes traditional models to handle sets of probability distributions, and presents sound abstraction procedures for three action types.
This paper provides a formal and practical framework for sound abstraction of probabilistic actions. We start by precisely defining the concept of sound abstraction within the context of finite-horizon planning (where each plan is a finite sequence of actions). Next we show that such abstraction cannot be performed within the traditional probabilistic action representation, which models a world with a single probability distribution over the state space. We then present the constraint mass assignment representation, which models the world with a set of probability distributions and is a generalization of mass assignment representations. Within this framework, we present sound abstraction procedures for three types of action abstraction. We end the paper with discussions and related work on sound and approximate abstraction. We give pointers to papers in which we discuss other sound abstraction-related issues, including applications, estimating loss due to abstraction, and automatically generating abstraction hierarchies.