Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs (Extended Abstract)
Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs (Extended Abstract)
Masood Feyzbakhsh Rankooh, Tomi Janhunen
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Sister Conferences Best Papers. Pages 8399-8404.
https://doi.org/10.24963/ijcai.2024/930
We establish a novel relation between delete-free planning, an important task for the AI Planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans.
Keywords:
Knowledge Representation and Reasoning: KRR: Logic programming
Planning and Scheduling: General
Knowledge Representation and Reasoning: KRR: Causality
Knowledge Representation and Reasoning: KRR: Reasoning about actions