A Novel Transition Based Encoding Scheme for Planning as Satisfiability

Barták

Surynek

2021

SOCS

There exist planning algorithms that can quickly find sub-optimal plans even for large problems and planning algorithms finding optimal plans but only for smaller problems. We attempt to integrate both approaches. We present an anytime technique for improving plan quality (decreasing the plan makespan) via substituting parts of the plan by better sub-plans. The technique guarantees optimality though it is primarily intended to quickly improve plan quality. We experimentally compare various approaches to local improvements.

Section: Identifying and Optimizing Local Sub-problemsmentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

See 1 more Smart Citation

On Improving Plan Quality via Local Enhancements

Barták

Surynek

2021

SOCS

“…Two reasons for this popularity are the rapidly increasing power of SAT solvers, which are becoming more efficient year by year, and various improvements that have been made to the method since its introduction. Some examples of these improvements are new compact and efficient encodings (Huang, Chen, and Zhang 2010;Rintanen, Heljanko, and Niemelä 2006;Robinson et al 2009;Balyo 2013), better ways of scheduling the SAT solvers (Rintanen, Heljanko, and Niemelä 2006), specialized SAT solving heuristics for planning problems (Rintanen, Heljanko, and Niemelä 2006), and -most recently -using incremental SAT solving (Gocht and Balyo 2017).…”

Section: Introductionmentioning

confidence: 99%

PASAR — Planning as Satisfiability with Abstraction Refinement

Froleyks

Schreiber

2021

SOCS

One of the classical approaches to automated planning is the reduction to propositional satisfiability (SAT). Recently, it has been shown that incremental SAT solving can increase the capabilities of several modern encodings for SAT-based planning. In this paper, we present a further improvement to SAT-based planning by introducing a new algorithm named PASAR based on the principles of counterexample guided abstraction refinement (CEGAR). As an abstraction of the original problem, we use a simplified encoding where interference between actions is generally allowed. Abstract plans are converted into actual plans where possible or otherwise used as a counterexample to refine the abstraction. Using benchmark domains from recent International Planning Competitions, we compare our approach to different state-of-the-art planners and find that, in particular, combining PASAR with forward state-space search techniques leads to promising results.

“…This is partly due to the power of SAT solvers, which are getting more efficient year by year. Since then many new improvements have been made to the method, such as new compact and efficient encodings (Huang, Chen, and Zhang 2010;Rintanen, Heljanko, and Niemelä 2006;Robinson et al 2009;Balyo 2013).…”

Section: Introductionmentioning

confidence: 99%

Accelerating SAT Based Planning with Incremental SAT Solving

Gocht

2017

ICAPS

One of the most successful approaches to automated planning is the translation to propositional satisfiability (SAT). We employ incremental SAT solving to increase the capabilities of several modern encodings for SAT based planning. Experiments based on benchmarks from the 2014 International Planning Competition show that an incremental approach significantly outperforms non incremental solving. Although we are using sequential scheduling of makespans, we can outperform the state-of-the-art SAT based planning system Madagascar in the number of solved instances.