Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions

• URL: http://arxiv.org/abs/2509.03953v2
• Date: Mon, 22 Sep 2025 11:45:16 GMT
• Title: Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions
• Authors: Ángel Aso-Mollar, Diego Aineto, Enrico Scala, Eva Onaindia,
• Abstract summary: In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables.<n>Existing state-of-the-art approaches have primarily handled control parameters as embedded constraints alongside other temporal and numeric restrictions.<n>We propose an efficient alternative that explicitly handles control parameters as true decision points within a systematic search scheme.
• Score: 11.009425634308043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables. Existing state-of-the-art approaches have primarily handled control parameters as embedded constraints alongside other temporal and numeric restrictions, and thus have implicitly treated them as additional constraints rather than as decision points in the search space. In this paper, we propose an efficient alternative that explicitly handles control parameters as true decision points within a systematic search scheme. We develop a best-first, heuristic search algorithm that operates over infinite decision spaces defined by control parameters and prove a notion of completeness in the limit under certain conditions. Our algorithm leverages the concept of delayed partial expansion, where a state is not fully expanded but instead incrementally expands a subset of its successors. Our results demonstrate that this novel search algorithm is a competitive alternative to existing approaches for solving planning problems involving control parameters.

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