How Hard is a Commercial Puzzle: the Eternity II Challenge

Recently, edge matching puzzles, an NP-complete problem, have received, thanks to money-prized contests, considerable attention from wide audiences. We consider these competitions not only a challenge for SAT/CSP solving techniques but also as an opportunity to showcase the advances in the SAT/CSP community to a general audience. This paper studies the NP-complete problem of edge matching puzzles focusing on providing generation models of problem instances of variable hardness and on its resolution through the application of SAT and CSP techniques. From the generation side, we also identify the phase transition phenomena for each model. As solving methods, we employ both; SAT solvers through the translation to a SAT formula, and two ad-hoc CSP solvers we have developed, with different levels of consistency, employing several generic and specialized heuristics. Finally, we conducted an extensive experimental investigation to identify the hardest generation models and the best performing solving techniques.

[1]  Wei Li,et al.  Exact Phase Transitions in Random Constraint Satisfaction Problems , 2000, J. Artif. Intell. Res..

[2]  Carlos Ansótegui,et al.  The Impact of Balancing on Problem Hardness in a Highly Structured Domain , 2006, AAAI.

[3]  Robert M. Haralick,et al.  Increasing Tree Search Efficiency for Constraint Satisfaction Problems , 1979, Artif. Intell..

[4]  Toby Walsh,et al.  Tetravex is NP-complete , 2006, Inf. Process. Lett..

[5]  Martin E. Dyer,et al.  Locating the Phase Transition in Binary Constraint Satisfaction Problems , 1996, Artif. Intell..

[6]  Chu Min Li,et al.  Heuristics Based on Unit Propagation for Satisfiability Problems , 1997, IJCAI.

[7]  Ian P. Gent,et al.  Watched Literals for Constraint Propagation in Minion , 2006, CP.

[8]  Armin Biere,et al.  Effective Preprocessing in SAT Through Variable and Clause Elimination , 2005, SAT.

[9]  Patrick Prosser,et al.  An Empirical Study of Phase Transitions in Binary Constraint Satisfaction Problems , 1996, Artif. Intell..

[10]  Jean-Charles Régin,et al.  A Filtering Algorithm for Constraints of Difference in CSPs , 1994, AAAI.

[11]  Christian Bessiere,et al.  MAC and Combined Heuristics: Two Reasons to Forsake FC (and CBJ?) on Hard Problems , 1996, CP.

[12]  Bart Selman,et al.  Generating Satisfiable Problem Instances , 2000, AAAI/IAAI.

[13]  Niklas Sörensson,et al.  An Extensible SAT-solver , 2003, SAT.

[14]  Erik D. Demaine,et al.  Jigsaw Puzzles, Edge Matching, and Polyomino Packing: Connections and Complexity , 2007, Graphs Comb..

[15]  Jean-Charles Régin The Symmetric Alldiff Constraint , 1999, IJCAI.

[16]  Andrei A. Bulatov,et al.  On the Power of k -Consistency , 2007, ICALP.

[17]  Niklas Sörensson,et al.  Translating Pseudo-Boolean Constraints into SAT , 2006, J. Satisf. Boolean Model. Comput..

[18]  Matti Järvisalo Further Investigations into Regular XORSAT , 2006, AAAI.

[19]  Rémi Monasson,et al.  Determining computational complexity from characteristic ‘phase transitions’ , 1999, Nature.

[20]  Carlos Ansótegui,et al.  Modeling Choices in Quasigroup Completion: SAT vs. CSP , 2004, AAAI.

[21]  Carlos Ansótegui,et al.  Mapping many-valued CNF formulas to Boolean CNF formulas , 2005, 35th International Symposium on Multiple-Valued Logic (ISMVL'05).

[22]  Rina Dechter,et al.  Constraint Processing , 1995, Lecture Notes in Computer Science.

[23]  Petteri Kaski,et al.  Hard Satisfiable Clause Sets for Benchmarking Equivalence Reasoning Techniques , 2006, J. Satisf. Boolean Model. Comput..