Abstracting Probabilistic Actions
This work addresses abstraction techniques for probabilistic actions in planning, but it appears incremental as it builds on existing representations without introducing a new paradigm.
The paper tackles the problem of abstracting conditional probabilistic actions by identifying intra-action and inter-action abstraction types, defining correctness criteria, and deriving four correct abstraction methods, which are applied to reduce planning complexity in the DRIPS decision-theoretic planner.
This paper discusses the problem of abstracting conditional probabilistic actions. We identify two distinct types of abstraction: intra-action abstraction and inter-action abstraction. We define what it means for the abstraction of an action to be correct and then derive two methods of intra-action abstraction and two methods of inter-action abstraction which are correct according to this criterion. We illustrate the developed techniques by applying them to actions described with the temporal action representation used in the DRIPS decision-theoretic planner and we describe how the planner uses abstraction to reduce the complexity of planning.