Incremental Inprocessing in SAT Solving

Fazekas, Katalin; Biere, Armin; Scholl, Christoph

doi:10.1007/978-3-030-24258-9_9

Cited by 19 publications

(12 citation statements)

References 36 publications

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“…However, Algorithm 3 is not compatible with incremental SAT solving (Fazekas, Biere, & Scholl, 2019) as those unit witnesses are clearly dependent on each other. It is possible to simply use the full autarky as witness instead, but that blows up the reconstruction stack (quadratically in the worst case) particularly if the autarky is large.…”

Section: Model Reconstructionmentioning

confidence: 99%

Better Decision Heuristics in CDCL through Local Search and Target Phases

Zhang¹,

Fleury²,

Biere³

2022

jair

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On practical applications, state-of-the-art SAT solvers dominantly use the conflict-driven clause learning (CDCL) paradigm. An alternative for satisfiable instances is local search solvers, which is more successful on random and hard combinatorial instances. Although there have been attempts to combine these methods in one framework, a tight integration which improves the state of the art on a broad set of application instances has been missing. We present a combination of techniques that achieves such an improvement. Our first contribution is to maximize in a local search fashion the assignment trail in CDCL, by sticking to and extending promising assignments via a technique called target phases. Second, we relax the CDCL framework by again extending promising branches to complete assignments while ignoring conflicts. These assignments are then used as starting point of local search which tries to find improved assignments with fewer unsatisfied clauses. Third, these improved assignments are imported back to the CDCL loop where they are used to determine the value assigned to decision variables. Finally, the conflict frequency of variables in local search can be exploited during variable selection in branching heuristics of CDCL. We implemented these techniques to improve three representative CDCL solvers (Glucose, MapleLcm DistChronoBT, and Kissat). Experiments on benchmarks from the main tracks of the last three SAT Competitions from 2019 to 2021 and an additional benchmark set from spectrum allocation show that the techniques bring significant improvements, particularly and not surprisingly, on satisfiable real-world application instances. We claim that these techniques were essential to the large increase in performance witnessed in the SAT Competition 2020 where Kissat and Relaxed LcmdCbDl NewTech were leading the field followed by CryptoMiniSAT-Ccnr, which also incorporated similar ideas.

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Section: Model Reconstructionmentioning

confidence: 99%

Better Decision Heuristics in CDCL through Local Search and Target Phases

Zhang¹,

Fleury²,

Biere³

2022

jair

Self Cite

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“…Since the SAT solver is called multiple times when solving a single MAPF problem, we call it directly through its API. This SAT solver also supports incremental SAT solving (Fazekas, Biere, & Scholl, 2019;Audemard et al, 2013), that is, it is possible to add variables and clauses incrementally between calls, and the solver is able utilize this to learn clauses and speedup its search.…”

Section: Basic-sat Mdd-satmentioning

confidence: 99%

Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

Surynek¹,

Stern²,

Boyarski³

et al. 2022

jair

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In the multi-agent path finding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions, aiming to minimize a given objective function. Many MAPF solvers were introduced in the past decade for optimizing two specific objective functions: sum-of-costs and makespan. Two prominent categories of solvers can be distinguished: search-based solvers and compilation-based solvers. Search-based solvers were developed and tested for the sum-of-costs objective, while the most prominent compilation-based solvers that are built around Boolean satisfiability (SAT) were designed for the makespan objective. Very little is known on the performance and relevance of solvers from the compilation-based approach on the sum-of-costs objective. In this paper, we start to close the gap between these cost functions in the compilation-based approach. Our main contribution is a new SAT-based MAPF solver called MDD-SAT, that is directly aimed to optimally solve the MAPF problem under the sum-of-costs objective function. Using both a lower bound on the sum-of-costs and an upper bound on the makespan, MDD-SAT is able to generate a reasonable number of Boolean variables in our SAT encoding. We then further improve the encoding by borrowing ideas from ICTS, a search-based solver. In addition, we show that concepts applicable in search-based solvers like ICTS and ICBS are applicable in the SAT-based approach as well. Specifically, we integrate independence detection, a generic technique for decomposing an MAPF instance into independent subproblems, into our SAT-based approach, and we design a relaxation of our optimal SAT-based solver that results in a bounded suboptimal SAT-based solver. Experimental evaluation on several domains shows that there are many scenarios where our SAT-based methods outperform state-of-the-art sum-of-costs search-based solvers, such as variants of the ICTS and ICBS algorithms.

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“…For the shortest-track the model checking problem is solved with the Bounded Model Checker CaMiCaL [6]. In Bounded Model Checking (BMC) the transition function of a circuit is encoded into a SAT formula and copied a number of times (called makespan).…”

Section: Encoding and Solvingmentioning

confidence: 99%

“…Since the makespan is incremented step-wise, the first solution found is guaranteed to be the shortest. CaMiCaL is highly integrated with the incremental SAT solver CaDiCaL [2] to optimize the effectiveness of incremental inprocessing [6].…”

Section: Encoding and Solvingmentioning

confidence: 99%