On the Configuration of SAT Formulae

It is well-known that the order in which clauses and literals are listed in a SAT formulae can have a strong impact on solvers’ performance.

[1]  James M. Crawford,et al.  Experimental Results on the Application of Satisfiability Algorithms to Scheduling Problems , 1994, AAAI.

[2]  Barry O'Sullivan,et al.  ReACTR: Realtime Algorithm Configuration through Tournament Rankings , 2015, IJCAI.

[3]  Kevin Leyton-Brown,et al.  Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.

[4]  Massimiliano Giacomin,et al.  On the impact of configuration on abstract argumentation automated reasoning , 2018, Int. J. Approx. Reason..

[5]  Jussi Rintanen Engineering Efficient Planners with SAT , 2012, ECAI.

[6]  Dawson R. Engler,et al.  KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex Systems Programs , 2008, OSDI.

[7]  Bart Selman,et al.  Backdoors To Typical Case Complexity , 2003, IJCAI.

[8]  Lukás Chrpa,et al.  On the effective configuration of planning domain models , 2015 .

[9]  Kevin Leyton-Brown,et al.  Tradeoffs in the empirical evaluation of competing algorithm designs , 2010, Annals of Mathematics and Artificial Intelligence.

[10]  Bart Selman,et al.  Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems , 2000, Journal of Automated Reasoning.

[11]  Kevin Leyton-Brown,et al.  Selection and Configuration of Parallel Portfolios , 2018, Handbook of Parallel Constraint Reasoning.

[12]  Yuri Malitsky,et al.  ISAC - Instance-Specific Algorithm Configuration , 2010, ECAI.

[13]  Holger H. Hoos,et al.  Captain Jack: New Variable Selection Heuristics in Local Search for SAT , 2011, SAT.

[14]  Armando Tacchella,et al.  (In)Effectiveness of Look-Ahead Techniques in a Modern SAT Solver , 2003, CP.

[15]  Marius Thomas Lindauer,et al.  SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers , 2015, SAT.

[16]  Gilles Audemard,et al.  Improving Glucose for Incremental SAT Solving with Assumptions: Application to MUS Extraction , 2013, SAT.

[17]  Armin Biere,et al.  A survey of recent advances in SAT-based formal verification , 2005, International Journal on Software Tools for Technology Transfer.

[18]  Marius Thomas Lindauer,et al.  The Configurable SAT Solver Challenge (CSSC) , 2015, Artif. Intell..

[19]  Kevin Leyton-Brown,et al.  An Efficient Approach for Assessing Hyperparameter Importance , 2014, ICML.

[20]  Kevin Leyton-Brown,et al.  Algorithm runtime prediction: Methods & evaluation , 2012, Artif. Intell..

[21]  Kevin Leyton-Brown,et al.  SATenstein: Automatically Building Local Search SAT Solvers from Components , 2009, IJCAI.

[22]  Kevin Leyton-Brown,et al.  Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection , 2010, AAAI.

[23]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[24]  Frederick P. Brooks,et al.  No Silver Bullet: Essence and Accidents of Software Engineering , 1987 .

[25]  Ivan Serina,et al.  A General Approach for Configuring PDDL Problem Models , 2018, ICAPS.

[26]  Armando Tacchella,et al.  Dependent and Independent Variables in Propositional Satisfiability , 2002, JELIA.