Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Weak-commitment search for solving constraint satisfaction problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Nogood Backmarking with Min-Conflict Repair in Constraint Satisfaction and Optimization
PPCP '94 Proceedings of the Second International Workshop on Principles and Practice of Constraint Programming
Journal of Artificial Intelligence Research
A new look at the easy-hard-easy pattern of combinatorial search difficulty
Journal of Artificial Intelligence Research
Performance test of local search algorithms using new types of random CNF formulas
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A Hybrid Seachr Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
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This paper explores the performance of a new complete non-systematic search algorithm learn-SAT on two types of 3-SAT problems, (i) an extended range of AIM problems [1] and (ii) structured unsolvable problems [2]. These are thought to present a difficult challenge for non-systematic search algorithms. They have been extensively used to study powerful special purpose SAT algorithms. We consider two of these, viz. the tableau-based algorithm of Bayardo & Schrag [2] and relsat. We compare their performance with that of learn-SAT, which is based on restart-repair and learning no-goods. Surprisingly, learn-SAT does very well. Sometimes it does much better than the other two algorithms; at other times they are broadly equivalent; and then there are some "anomalies". One thing at least is clear, learn-SAT solves problems which many would predict are beyond its scope. The relative performance of the three algorithms generates several interesting questions. We point to some of them with a view to future research. The empirical paradigm in this paper reflect some of the views outlined by Mammen & Hogg [10].