Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Learning dynamic algorithm portfolios
Annals of Mathematics and Artificial Intelligence
The Second Answer Set Programming Competition
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
On the power of clause-learning SAT solvers with restarts
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Continuous Search in Constraint Programming
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Potassco: The Potsdam Answer Set Solving Collection
AI Communications - Answer Set Programming
Conflict-driven answer set solving: From theory to practice
Artificial Intelligence
The multi-engine ASP solver ME-ASP
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Evaluating tree-decomposition based algorithms for answer set programming
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Quantifying homogeneity of instance sets for algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Asp modulo csp: The clingcon system
Theory and Practice of Logic Programming
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We propose a portfolio-based solving approach to Answer Set Programming (ASP). Our approach is homogeneous in considering several configurations of the ASP solver clasp. The selection among the configurations is realized via Support Vector Regression. The resulting portfolio-based solver claspfolio regularly outperforms clasp's default configuration as well as manual tuning.