Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version)

2026-05-28

AI Summary

This work investigates the efficient encoding of factored transition systems (FTS) featuring disjunctive preconditions, conditional effects, and angelic nondeterminism into SAT problems to enhance the performance of SAT-based planning. We systematically explore a range of logical encoding strategies, integrating task transformations and parallelization mechanisms, and present the first comprehensive analysis of their impact on SAT planner efficiency. Our experiments reveal that specific encodings interact critically with problem structure to accelerate solving, while also uncovering counterexamples where certain formulations degrade performance. These findings offer both theoretical insights and practical guidelines for effectively translating FTS instances into SAT, thereby advancing the state of the art in declarative planning.

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.

Problem

• factored tasks
• SAT encoding
• planning
• transition relation
• task transformation

Innovation

• factored tasks
• SAT encoding
• transition relation
• parallelism
• task transformation