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Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning (Extended Abstract)

David Speck, Daniel Gnad

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers. Pages 10932-10936.

https://doi.org/10.24963/ijcai.2025/1219

Keywords

  • Sister Conferences Best Papers: Planning and Scheduling

Abstract

Classical planning provides a framework for solving sequential decision-making problems, i.e., finding a sequence of actions that transforms the current state of the world into a state that satisfies a desired goal condition. Planning tasks are modeled in a logic that describes the environment and its dynamics. It is well known that the specific problem formulation can significantly affect the performance of planning systems solving problems like the Rubik's Cube or finding algorithms for matrix multiplication. In this work, we propose a domain-general problem reformulation that embodies decoupled search, a search-reduction technique from classical planning and model checking. Decoupled search decomposes a given problem to exploit its structure, achieving exponential reductions over other search techniques. We show that decoupled search can be captured exactly as a task reformulation and that, on many benchmark domains, it performs as good and sometimes even better than a native decoupled-search implementation.

David Speck, and Daniel Gnad. 2025. Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning (Extended Abstract). In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10932–10936. https://doi.org/10.24963/ijcai.2025/1219
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