Abstract
Numeric planning is known to be undecidable even under severe restrictions. Prior work has investigated the decidability boundaries by restricting the expressiveness of the planning formalism in terms of the numeric functions allowed in conditions and effects. We study a well-known restricted form of Hoffmann’s simple numeric planning, which is undecidable. We analyze the complexity by imposing restrictions on the causal structure, exploiting a novel method for bounding variable domain sizes. First, we show that plan existence for tasks where all numeric variables are root nodes in the causal graph is in PSPACE. Second, we show that for tasks with only numeric leaf variables the problem is decidable, and that it is in PSPACE if the propositional state space has a fixed size. Our work lays a strong foundation for future investigations of structurally more complex tasks. From a practical perspective, our method allows to employ heuristics and methods that are geared towards finite variable domains (such as pattern database heuristics or decoupled search) to solve non-trivial families of numeric planning problems.
Original language | English |
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Title of host publication | AAAI-23 Technical Tracks 10 |
Editors | Brian Williams, Yiling Chen, Jennifer Neville |
Publisher | AAAI press |
Pages | 12112-12119 |
Number of pages | 8 |
ISBN (Electronic) | 9781577358800 |
State | Published - 27 Jun 2023 |
Event | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023 |
Publication series
Name | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
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Volume | 37 |
Conference
Conference | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
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Country/Territory | United States |
City | Washington |
Period | 7/02/23 → 14/02/23 |
Bibliographical note
Publisher Copyright:Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funding
The work of Alexander Shleyfman was partially supported by the Israel Academy of Sciences and Humanities program for Israeli postdoctoral researchers. Daniel Gnad was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by TAILOR, a project funded by the EU Horizon 2020 research and innovation programme under grant agreement no. 952215. Peter Jons-son was partially supported by the Swedish Research Council (VR) under grant 2021-04371.
Funders | Funder number |
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Israel Academy of Sciences and Humanities program for Israeli | |
TAILOR | |
Knut och Alice Wallenbergs Stiftelse | |
Vetenskapsrådet | 2021-04371 |
Horizon 2020 | 952215 |