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

Researchers propose novel methods for encoding factored tasks—a compact planning representation—into SAT (Boolean satisfiability) problems, moving beyond traditional heuristic search approaches. The work examines multiple encoding strategies and analyzes how task transformations and parallelism affect SAT-based planner performance, advancing computational planning techniques.

This research addresses a foundational challenge in automated planning by bridging factored task representations with SAT solving technology. Factored tasks extend standard planning formalisms like STRIPS and SAS+ by supporting disjunctive preconditions, conditional effects, and angelic nondeterminism in a more compact form. Previously, planning systems relied exclusively on heuristic search methods to handle these representations, limiting both computational efficiency and solution scalability.

The shift toward SAT-based encoding represents a significant methodological advancement in computer science. SAT solvers have matured substantially over the past two decades, with industrial-strength implementations capable of handling millions of variables and clauses. By translating factored task planning into propositional logic, researchers can leverage these optimized solvers rather than developing domain-specific algorithms. This approach democratizes access to high-performance planning tools across different problem domains.

The practical implications extend to artificial intelligence systems requiring automated decision-making under complex constraints. Applications span robotics, autonomous systems, resource allocation, and game AI, where efficient planning directly impacts real-world performance. The investigation into parallelism exploitation recognizes modern computing's multicore architecture, potentially accelerating solution times significantly.

The systematic analysis of task transformation impacts provides critical guidance for practitioners optimizing encoding strategies. Different transformations may benefit or hinder SAT solver performance depending on the underlying problem structure. Future work should focus on developing heuristics for selecting optimal encoding strategies dynamically and extending these methods to temporal and probabilistic planning domains.

• →Factored tasks enable more compact planning representations than traditional STRIPS/SAS+ formalisms
• →SAT-based encoding provides an alternative to heuristic search, leveraging mature SAT solver technology
• →Multiple encoding strategies exist for translating factored transition relations, with varying performance characteristics
• →Parallelism exploitation at multiple levels offers potential for computational speedup
• →Task transformations significantly impact SAT solver efficiency and require careful selection