Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions
This work addresses a specific bottleneck in automated planning for researchers and practitioners, offering an incremental improvement over current approaches.
The paper tackles the problem of handling infinite domain control parameters in automated planning by proposing a best-first search algorithm with delayed partial expansions, which treats parameters as decision points and proves completeness under certain conditions, showing it is a competitive alternative to existing methods.
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.