Axioms in Model-based Planners
This work addresses the challenge of efficiently handling derived predicates in planning for AI researchers, but it is incremental as it extends previous state-space search methods to new computational approaches.
The paper tackled the problem of using axioms in domain-independent planning models to reduce search spaces and plan lengths, and proposed axiom-aware planners based on answer set programming and integer programming, showing they can exploit axioms' expressivity in PDDL domains.
Axioms can be used to model derived predicates in domain- independent planning models. Formulating models which use axioms can sometimes result in problems with much smaller search spaces and shorter plans than the original model. Previous work on axiom-aware planners focused solely on state- space search planners. We propose axiom-aware planners based on answer set programming and integer programming. We evaluate them on PDDL domains with axioms and show that they can exploit additional expressivity of axioms.