翻訳と辞書 Words near each other ・ Constraint ・ Constraint (classical mechanics) ・ Constraint (computer-aided design) ・ Constraint (information theory) ・ Constraint (mathematics) ・ Constraint algebra ・ Constraint algorithm ・ Constraint automaton ・ Constraint Composite Graph ・ Constraint counting ・ Constraint Grammar ・ Constraint graph ・ Constraint graph (layout) ・ Constraint Handling Rules ・ Constraint inference ・ Constraint learning ・ Constraint logic programming ・ Constraint programming ・ Constraint satisfaction ・ Constraint satisfaction dual problem ・ Constraint satisfaction problem ・ Constraint-based Routing Label Distribution Protocol ・ Constraint-induced movement therapy ・ Constraints accounting ・ Constricta ・ Constricta (fungus) ・ Constricted elimia ・ Constricting Rage of the Merciless ・ Constriction ・ Constriction of video Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Constraint learning ： ウィキペディア英語版 Constraint learning In constraint satisfaction backtracking algorithms, constraint learning is a technique for improving efficiency. It works by recording new constraints whenever an inconsistency is found. This new constraint may reduce the search space, as future partial evaluations may be found inconsistent without further search. Clause learning is the name of this technique when applied to propositional satisfiability. ==Definition== Backtracking algorithms work by choosing an unassigned variable and recursively solve the problems obtained by assigning a value to this variable. Whenever the current partial solution is found inconsistent, the algorithm goes back to the previously assigned variable, as expected by recursion. A constraint learning algorithm differs because it tries to record some information, before backtracking, in form of a new constraint. This can reduce the further search because the subsequent search may encounter another partial solution that is inconsistent with this new constraint. If the algorithm has learned the new constraint, it will backtrack from this solution, while the original backtracking algorithm would do a subsequent search. If the partial solution $x\_1=a\_1,\backslash ldots,x\_k=a\_k$ is inconsistent, the problem instance implies the constraint stating that $x\_i=a\_i$ cannot be true for all $i\; \backslash in\; ()$ at the same time. However, recording this constraint is not useful, as this partial solution will not be encountered again due to the way backtracking proceeds. On the other hand, if a subset of this evaluation is inconsistent, the corresponding constraint may be useful in the subsequent search, as the same subset of the partial evaluation may occur again in the search. For example, the algorithm may encounter an evaluation extending the subset $x\_2=a\_2,\; x\_5=a\_5,\; x\_=a\_$ of the previous partial evaluation. If this subset is inconsistent and the algorithm has stored this fact in form of a constraint, no further search is needed to conclude that the new partial evaluation cannot be extended to form a solution. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Constraint learning」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.