Nogood learning is a deductive learning technique used for the purpose of intelligent backtrackings in constraint satisfaction. The approach analyzes failures at backtracking points and derives sets of variable bindings, or nogoods, that will never lead to a solution. These nogood constraints can then be used to prune later search nodes.
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(2017). Nogood Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_593
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_593
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