PDDL+ Planning via Constraint Answer Set Programming
This addresses planning in hybrid domains for AI researchers, representing an incremental advance as the first link between PDDL+ planning and logic programming.
The authors tackled the problem of planning with mixed discrete-continuous dynamics in PDDL+ by developing a new approach using Constraint Answer Set Programming (CASP), encoding PDDL+ models into CASP problems and achieving promising results on benchmark problems with the EZCSP solver.
PDDL+ is an extension of PDDL that enables modelling planning domains with mixed discrete-continuous dynamics. In this paper we present a new approach to PDDL+ planning based on Constraint Answer Set Programming (CASP), i.e. ASP rules plus numerical constraints. To the best of our knowledge, ours is the first attempt to link PDDL+ planning and logic programming. We provide an encoding of PDDL+ models into CASP problems. The encoding can handle non-linear hybrid domains, and represents a solid basis for applying logic programming to PDDL+ planning. As a case study, we consider the EZCSP CASP solver and obtain promising results on a set of PDDL+ benchmark problems.