MaxSAT , Hard and Soft ConstraintsMany, Felip
This paper presents open-wbo, a new MaxSAT solver. open-wbo has two main features. First, it is an open-source solver that can be easily modified and extended. Most MaxSAT solvers are not available in open-source, making it hard to extend and improve cur
Selman, “Solving problems with hard and soft constraints using a stochastic algorithm for MAX-SAT,” presented at the First International Joint Workshop on Artificial Intelligence and Operations Research, Timberline, OR, 1995. S. Joy, J.E. Mitchell, and B. Borchers, “A branch-and-cut ...
Solving over-constrained problems with Max-SAT solvers typically consists of finding an assignment that satisfies all the hard constraints and the maximum number of soft constraints. Despite the relevance of clause learning in SAT for solving structured instances, this technology has not yet been exten...
problems, because they indirectly exploit clause learning via the SAT solver. 译文:基于SAT的MaxSAT算法在解决许多现实世界NP-hard优化问题时的性能通常比BnB MaxSAT算法要好得多,因为它们间接利用了SAT求解器的子句学习。 Unfortunately,it is hard for a BnB solver to exploit clause learning. 译文:不幸的是...
A general stochastic approach to solving problems with hard and soft constraints D. Du, J. Gu, P.M. Pardalos (Eds.), The Satisfiability Problem: Theory and Applications (DIMACS Workshop March 11–13, 1996), DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 35, Ame...
all being soft constraint with a weight of 5. The last two lines say that v1 XOR x2 = true and v1 XOR v3 XOR v4 = false, both of which have a weight of 10, and are hard constraints. When you run the tool on the problem above, you get the following output: git clone https:...
"A Two Level Local Search for MAX-SAT Problems with Hard and Soft Constraints". In: The 15th Aus- tralian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence, v. 2557, pp. 603-614, London, UK, 2002. Springer-Verlag....
The models typically consist of hard and soft constraints, or combine hard constraints with a preference function. We introduce a novel setting for learning combinatorial optimisation problems from contextual examples. These positive and negative examples show - in a particular context - whether the ...
For these problems, we consider as well the two extensions of the highest practical interest, namely the inclusion of weights to clauses, and the distinction between hard (mandatory) and soft (desirable) constraints. Hence, our methods handle any subclass of the most general variants: Weighted ...