Planning with Uncertainty: Symmetries, Policy Inference, and Solution Compression

ArXi:2403.19883v2 Announce Type: replace Fully-observable non-deterministic (FOND) planning is at the core of artificial intelligence planning with uncertainty. It models uncertainty through actions with non-deterministic effects. In this work, we present a collection of techniques that establish explicit best-first policy-space search as a method competitive with the state of the art for solving FOND planning tasks. We study how to define equivalence relations between policies, allowing part of the search space to be pruned.