Learning Variable Descriptors for Applying Heuristics Across CSP Problems
作者： David S. Day
DOI: 10.1016/B978-1-55860-200-7.50029-5
关键词: Constraint learning 、 Variable (computer science) 、 Basis (linear algebra) 、 Constraint (information theory) 、 Heuristics 、 Variety (linguistics) 、 Constraint satisfaction problem 、 Machine learning 、 Space (commercial competition) 、 Mathematics 、 Artificial intelligence
摘要: Many naturally occurring problems can be usefully represented as constraint satisfaction for which a variety of general purpose algorithms are available. However, to date the ability improve problem solving performance with experience has been largely limited learning constraints prune search space or other value-ordering heuristics whose applicability is restricted completely identical networks. In this research we show how formed using language that allows their application across very different instances. This incrementally on basis variables seen in course problems.
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