Parallel strategies for best-first generalized planning

Fernández Alburquerque, Alejandro

Parallel strategies for best-first generalized planning

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Citation

Fernández Alburquerque, A. Parallel strategies for best-first generalized planning. 2024. handle: http://hdl.handle.net/10230/61315

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Abstract

In recent years, Artificial Intelligence (AI) has become the big trend in computer science, and many related areas are seeing renewed interest. One of these areas is Generalized Planning (GP), which studies the automated synthesis of algorithmicl ike solutions capable of solving multiple classical planning instances. Tightly coupled with the success of AI, there has been a steady increase in computational power thanks to multi-core CPUs and GPUs. This has encouraged active research in parallel programming, which is needed to use the full potential of current hardware. In this work, we explore parallelization strategies for tree-search algorithms. Furthermore, we propose one algorithm to parallelize Best-First Generalized Planning (BFGP), a heuristic search approach to GP. We show that our algorithm can scale linearly with the number of processors, but we also discuss some cases in which pathological behavior can cause performance degradation.