Action Policy Explanations in Oversubscription Planning

Aleena Siji, Rebecca Eifler, Daniel Fišer, Joerg Hoffmann

Keywords: policy, explanations, OSP

Abstract:

Oversubscription planning (OSP) is a planning formalism that can deal with planning scenarios where not all goals can be achieved. If the global optimization goal is not fixed, an iterative process in which users refine their preferences based on the sample plans is suitable. To help the users, the planning systems should be able to provide answers to questions such as ``Why is goal $g$ not satisfied by the sample
plan?''

In this paper, we focus on explaining the behavior of a given black-box policy under the aforementioned planning scenario. That is, we assume that sample plans are provided by a state-dependent deterministic policy $\phi$ (in our case a neural network
policy), and we try to automatically answer the question ``Why is the goal $g$ not satisfied by $\phi$?'' by providing information how much
the policy $\phi$ would need to diverge from its decision in order to satisfy the goal $g$. Moreover, we also provide information in which state and how the policy should diverge. To this end, we extended action policies based on action schema networks to
support OSP tasks and design an algorithm that is able to provide the desired explanations. We evaluate the performance of the proposed algorithm and provide a case study with sample explanations.