Projection Abstractions in Planning Under the Lenses of Abstractions for MDPs

TL;DR

This paper unifies the concept of projection abstractions in AI Planning and discounted MDPs, revealing their similarities, differences, and potential for cross-field advancements.

Contribution

It bridges the gap between Planning and MDP abstractions by relating projection methods, highlighting their computational and representational trade-offs, and proposing new research directions.

Findings

Projection abstractions in Planning can be derived from MDP frameworks.

Unified view reveals commonalities and differences in abstraction assumptions.

Highlights potential for cross-disciplinary improvements in planning and decision-making.

Abstract

The concept of abstraction has been independently developed both in the context of AI Planning and discounted Markov Decision Processes (MDPs). However, the way abstractions are built and used in the context of Planning and MDPs is different even though lots of commonalities can be highlighted. To this day there is no work trying to relate and unify the two fields on the matter of abstractions unraveling all the different assumptions and their effect on the way they can be used. Therefore, in this paper we aim to do so by looking at projection abstractions in Planning through the lenses of discounted MDPs. Starting from a projection abstraction built according to Classical or Probabilistic Planning techniques, we will show how the same abstraction can be obtained under the abstraction frameworks available for discounted MDPs. Along the way, we will focus on computational as well as representational advantages and disadvantages of both worlds pointing out new research directions that are of interest for both fields.