Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version)
This paper investigates encoding factored tasks (FTS) in SAT, proposing several encoding strategies and analyzing the impact of task transformations and parallelism on SAT-based planner performance.
[2605.30563] Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version)
[Submitted on 28 May 2026]
Title:Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version)
View a PDF of the paper titled Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version), by Jo\~ao Filipe and 2 other authors
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Abstract:Factored tasks are a classical planning representation that extends SAS+ with limited forms of disjunctive preconditions, conditional effects, and angelic nondeterminism. This allows for a more compact representation of tasks than traditional formalisms such as STRIPS or SAS+, and supports a wide range of task transformations. However, existing planning approaches for factored tasks have been limited to heuristic search methods.
In this work, we investigate how to encode factored tasks in SAT. We propose several ways to encode the tasks, focusing on different strategies for translating the factored transition relation into propositional logic. We also analyze how to exploit parallelism at various levels in this setting and study the impact of common task transformations on the performance of SAT-based planners.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.30563 [cs.AI]
(or arXiv:2605.30563v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.30563
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gregor Behnke [view email] [v1] Thu, 28 May 2026 20:50:52 UTC (662 KB)
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