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Florian Pollitt

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4 papers

2 author rows

SAT Conference 2025 Conference Paper

Learn to Unlearn

Bernhard Gstrein
Florian Pollitt
André Schidler
Mathias Fleury
Armin Biere

Clause learning is a significant milestone in the development of SAT solving. However, keeping all learned clauses without discrimination gradually slows down the solver. Thus, selectively removing some learned clauses during routine database reduction is essential. In this paper, we reexamine and test several long-standing ideas for clause removal in the modern solver Kissat. Our experiments show that retaining all clauses alters performance in all instances. For satisfiable instances, periodically removing all learned clauses surprisingly yields near state-of-the-art performance. For unsatisfiable instances, it is vital to always keep some learned clauses. Building on the influential Glucose paper, we find that it is crucial to always retain the clauses most likely to help, regardless of whether they are ranked by size or LBD in practice. Another key factor is whether a clause was used recently during conflict resolution steps. Eagerly keeping used clauses improves all unlearning strategies.

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LPAR Conference 2024 Conference Paper

Certifying Incremental SAT Solving

Katalin Fazekas
Florian Pollitt
Mathias Fleury
Armin Biere

Certifying results by checking proofs and models is an essential feature of modern SAT solving. While incremental solving with assumptions and core extraction is crucial for many applications, support for incremental proof certificates remains lacking. We propose a proof format and corresponding checkers for incremental SAT solving. We further extend it to leverage resolution hints. Experiments on incremental SAT solving for Bounded Model Checking and Satisfiability Modulo Theories demonstrate the feasibility of our approach, further confirming that resolution hints substantially reduce checking time.

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SAT Conference 2023 Conference Paper

Faster LRAT Checking Than Solving with CaDiCaL

Florian Pollitt
Mathias Fleury
Armin Biere

DRAT is the standard proof format used in the SAT Competition. It is easy to generate but checking proofs often takes even more time than solving the problem. An alternative is to use the LRAT proof system. While LRAT is easier and way more efficient to check, it is more complex to generate directly. Due to this complexity LRAT is not supported natively by any state-of-the-art SAT solver. Therefore Carneiro and Heule proposed the mixed proof format FRAT which still suffers from costly intermediate translation. We present an extension to the state-of-the-art solver CaDiCaL which is able to generate LRAT natively for all procedures implemented in CaDiCaL. We further present Lrat-Trim, a tool which not only trims and checks LRAT proofs in both ASCII and binary format but also produces clausal cores and has been tested thoroughly. Our experiments on recent competition benchmarks show that our approach reduces time of proof generation and certification substantially compared to competing approaches using intermediate DRAT or FRAT proofs.

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AAAI Conference 2022 Conference Paper

Making Translations to Classical Planning Competitive with Other HTN Planners

Gregor Behnke
Florian Pollitt
Daniel Höller
Pascal Bercher
Ron Alford

Translation-based approaches to planning allow for solving problems in complex and expressive formalisms via the means of highly efficient solvers for simpler formalisms. To be effective, these translations have to be constructed appropriately. The current existing translation of the highly expressive formalism of HTN planning into the more simple formalism of classical planning is not on par with the performance of current dedicated HTN planners. With our contributions in this paper, we close this gap: we describe new versions of the translation that reach the performance of state-of-the-art dedicated HTN planners. We present new translation techniques both for the special case of totally-ordered HTNs as well as for the general partially-ordered case. In the latter, we show that our new translation generates only linearly many actions, while the previous encoding generates and exponential number of actions.

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