A Dynamic Epistemic Framework for Conformant Planning
This work addresses the challenge of conformant planning for AI systems, but it appears incremental as it builds on existing logical frameworks with specific algorithmic contributions.
The paper tackles the problem of automated planning under initial uncertainty by introducing a dynamic epistemic logical framework, reducing plan verification and conformant planning to model checking problems, and showing that the iteration-free fragment is PSPACE-complete.
In this paper, we introduce a lightweight dynamic epistemic logical framework for automated planning under initial uncertainty. We reduce plan verification and conformant planning to model checking problems of our logic. We show that the model checking problem of the iteration-free fragment is PSPACE-complete. By using two non-standard (but equivalent) semantics, we give novel model checking algorithms to the full language and the iteration-free language.