Symbolic Numeric Planning with Patterns - Cardellini, Matteo; Giunchiglia, Enrico; Maratea, Marco (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
In this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem Pi, a bound n and a pattern –defined as an arbitrary sequence of actions– we encode the problem of finding a plan for Pi with bound n as a formula with fewer variables and/or clauses than the state-of-the-art rolled-up and relaxed-relaxed-exists encodings. More importantly, we prove that for any given bound, it is never the case that the latter two encodings allow finding a valid plan while ours does not. On the experimental side, we consider 6 other planning systems –including the ones which participated in this year’s International Planning Competition (IPC)– and we show that our planner Patty has remarkably good comparative performances on this year’s IPC problems.