Logic programs with stable model semantics as a constraint programming paradigm
Annals of Mathematics and Artificial Intelligence
Enhancing DLV instantiator by backjumping techniques
Annals of Mathematics and Artificial Intelligence
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
Grounding for model expansion in k-guarded formulas with inductive definitions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Clone: solving weighted Max-SAT in a reduced search space
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
SAT(ID): satisfiability of propositional logic extended with inductive definitions
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
NP-SPEC: an executable specification language for solving all problems in NP
Computer Languages
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In many real-life computational search problems, one is not only interested in finding a solution, but also in maintaining a solution under varying circumstances. For example, in the area of network configuration, an initial configuration of a computer network needs to be obtained, but also a new configuration when one of the machines in the network breaks down. Currently, most such revision problems are solved manually, or with highly specialized software. A recent declarative approach to solve (hard) computational search problems involving a lot of domain knowledge, is by finite model generation. Here, the domain knowledge is specified as a logic theory T, and models of T correspond to solutions of the problem. In this paper, we extend this approach to solve revision problems. In particular, our method allows to use the same theory to describe the search problem and the revision problem, and applies techniques from current model generators to find revised solutions.