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Lagrangian Decomposition for Classical Planning (Extended Abstract)
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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
Abstract
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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