Abstract
Solving planning problems via translation to satisfiability (SAT) is one of the most successful approaches to automated planning. We propose a new encoding scheme, called Reinforced Encoding, which encodes a planning problem represented in the SAS+ formalism into SAT. The Reinforced Encoding is a combination of the transition-based SASE encoding with the classical propositional encoding. In our experiments we compare our new encoding to other known SAS+ based encodings. The results indicate, that he Reinforced encoding performs well on the benchmark problems of the 2011 International Planning Competition and can outperform all the other known encodings for several domains.
Highlights
Planning is the problem of finding a sequence of actions – a plan, that transforms the world from an initial state to a state that satisfies some goal conditions
If we compare the encodings, we can observe that the R2∃-Step encoding has the highest total number of solved instances followed by our new Reinforced encoding
The Reinforced encoding achieves this for three domains, while the Direct and SASE encoding cannot outperform the other encod
Summary
Planning is the problem of finding a sequence of actions – a plan, that transforms the world from an initial state to a state that satisfies some goal conditions. One of the most successful approaches to planning is encoding the planning problem into a series of satisfiability (SAT) formulas and using a SAT solver to solve them. The method was first introduced by Kautz and Selman [1] and is still very popular and competitive This is partly due to the power of SAT solvers, which are getting more efficient year by year. Since many new improvements have been made to the method, such as new compact and efficient encodings [2,3,4,5], better ways of scheduling the SAT solvers [3] or modifying the SAT solver’s heuristics to be more suitable for solving planning problems [6]
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