
arXiv: 1505.01221
handle: 1887/58334 , 1887/58269
It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014.
FOS: Computer and information sciences, Computer Science - Machine Learning, propositional satisfiability, Computer Science - Artificial Intelligence, algorithm configuration, supervised learning, 004, Machine Learning (cs.LG), Artificial Intelligence (cs.AI), AI, SAT, Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.), empirical algorithmics, empirical evaluation, competition
FOS: Computer and information sciences, Computer Science - Machine Learning, propositional satisfiability, Computer Science - Artificial Intelligence, algorithm configuration, supervised learning, 004, Machine Learning (cs.LG), Artificial Intelligence (cs.AI), AI, SAT, Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.), empirical algorithmics, empirical evaluation, competition
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 39 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
