Lagrangian Decomposition for Classical Planning (Extended Abstract) release_ngkfzfxzu5afdftl2tuyo45uue

by Florian Pommerening, Gabriele Röger, Malte Helmert, Hadrien Cambazad, Louis-Martin Rousseau, Domenico Salvagnin

Published in International Joint Conference on Artificial Intelligence by International Joint Conferences on Artificial Intelligence Organization.

2020   p4730-4734


Optimal cost partitioning of classical planning heuristics has been shown to lead to excellent heuristic values but is often prohibitively expensive to compute. We analyze the application of Lagrangian decomposition, a classical tool in mathematical programming, to cost partitioning of operator-counting heuristics. This allows us to view the computation as an iterative process that can be seeded with any cost partitioning and that improves over time. In the case of non-negative cost partitioning of abstraction heuristics the computation reduces to independent shortest path problems and does not require an LP solver.
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