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タイトル: 系統的/局所的探索の協調によるファジィ制約充足問題の近似解法
その他のタイトル: Combining Systematic and Local Search for Approximately Solving Fuzzy Constraint Satisfaction Problems
著者: 須藤, 康裕 著作を一覧する
栗原, 正仁 著作を一覧する
キーワード: constraint satisfaction
iterative improvement
発行日: 2006年
出版者: 人工知能学会
誌名: 人工知能学会論文誌
巻: 21
号: 1
開始ページ: 20
終了ページ: 27
出版社 DOI: 10.1527/tjsai.21.20
抄録: A fuzzy constraint satisfaction problem (FCSP) is an extension of the classical CSP, a powerful tool for modeling various problems based on constraints among variables. Basically, the algorithms for solving CSPs are classified into two categories: the systematic search (complete methods based on search trees) and the local search (approximate methods based on iterative improvement). Both have merits and demerits. Recently, much attention has been paid to hybrid methods for integrating both merits to solve CSPs efficiently, but no such attempt has been made so far for solving FCSPs. In this paper, we present a hybrid, approximate method for solving FCSPs. The method, called the Spread-Repair-Shrink (SRS) algorithm, combines a systematic search with the Spread-Repair (SR) algorithm, a local search method recently developed by the authors. The SRS algorithm spreads (or expands) and shrinks a set of search trees in order to repair constraints locally until, finally, the satisfaction degree of the worst constraints (which are the roots of the trees) is improved. We empirically show that SRS outperforms the SR algorithm as well as the well-known methods such as Forward Checking and Fuzzy GENET, when the size of the problems is sufficiently large.
資料タイプ: article (author version)
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: 栗原 正仁


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