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BLAST: bit-blasting numbers for classical planning (extended abstract)

Abstract:
It is well known that numeric planning can be made decidable if the domain of all numeric state variables is finite. This bounded formulation can be polynomially compiled into classical planning with Boolean conditions and conditional effects preserving the plan size exactly. However, it remains unclear whether this compilation has any practical utility. To explore this aspect, this work revisits the theoretical compilation framework from a practical perspective, focusing on the fragment of simple numeric planning. Specifically, we introduce three different compilations. The first, called one-hot, aims to systematise the current practice among planning practitioners of modelling numeric planning through classical planning. The other two, termed binary compilations, extend and specialise the logarithmic encoding introduced in previous literature. Our experimental analysis reveals that the overly complex logarithmic encoding can, surprisingly, be made practical with some representational expedients. Among these, the use of axioms is particularly crucial. Furthermore, we identify a class of mildly numeric planning problems where a classical planner, i.e., LAMA, when run on the compiled problem, is highly competitive with state-of-the-art numeric planners.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1609/socs.v18i1.36002

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author


Publisher:
Association for the Advancement of Artificial Intelligence
Host title:
Proceedings of the International Symposium on Combinatorial Search
Volume:
18
Issue:
1
Pages:
249-250
Publication date:
2025-07-20
Acceptance date:
2025-06-03
Event title:
18th International Symposium on Combinatorial Search (SoCS 2025)
Event location:
University of Glasgow, Scotland, UK
Event website:
https://socs25.search-conference.org/
Event start date:
2025-08-12
Event end date:
2025-08-15
DOI:
EISSN:
2832-9163
ISSN:
2832-9171
ISBN-10:
1577359011
ISBN-13:
9781577359012


Language:
English
Pubs id:
2129752
Local pid:
pubs:2129752
Deposit date:
2025-06-13
ARK identifier:

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