Efficient Computation and Informative Estimation of h+ by Integer and Linear Programming
Keywords:Optimal Planning, Admissible Heuristics, LP-based Heuristics, Vertex Elimination, Acyclicity
AbstractWe investigate modeling cost optimal delete-free STRIPS Planning by Integer/Linear Programming (IP/LP). We introduce two IP models and their LP relaxations based on a recently formulated representation of relaxed plans, named causal relaxed plan representation. The new models are produced by enforcing acyclicity in so-called causal relation graphs using vertex elimination and time labeling methods. We empirically show that while the vertex elimination based method outperforms the time labeling based method and all previously introduced domain independent methods for computing the exact values of h+, the time labeling based LP model is faster to solve compared to its vertex elimination based alternative, making it more suitable for using as heuristic function for optimal planning. We also theoretically analyze the admissible heuristic functions obtained by solving our LP models, and prove that the vertex elimination based heuristic is at least as informative as the time labeling based heuristic. Moreover, our empirical analysis shows that our vertex elimination based heuristic, which is a novel admissible estimation of h+, often has information complementary to that of the LM-cut heuristic.
How to Cite
Feyzbakhsh Rankooh, M., & Rintanen, J. (2022). Efficient Computation and Informative Estimation of h+ by Integer and Linear Programming. Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 71-79. https://doi.org/10.1609/icaps.v32i1.19787