Projection Abstractions in Planning Under the Lenses of Abstractions for MDPs
This work addresses the lack of integration between AI Planning and MDPs on abstractions, which is incremental as it connects existing fields without introducing new methods.
The paper tackles the problem of unifying abstraction concepts in AI Planning and discounted Markov Decision Processes (MDPs) by analyzing projection abstractions in Planning through MDP frameworks, showing how similar abstractions can be derived and highlighting computational and representational trade-offs.
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.