Linear satisfiability preserving assignments

Kei Kimura, Kazuhisa Makino

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

In this paper, we study several classes of satisfiability preserving assignments to the constraint satisfaction problem. In particular, we consider fixable, autark and satisfying assignments. Since it is in general NP-hard to find a nontrivial (i.e., nonempty) satisfiability preserving assignment, we introduce linear satisfiability preserving assignments, which are defined by polyhedral cones in an associated vector space. The vector space is obtained by the identification, introduced by Kullmann, of assignments with real vectors. We consider arbitrary polyhedral cones, where only restricted classes of cones for autark assignments are considered in the literature. We reveal that cones in certain classes are maximal as a convex subset of the set of the associated vectors, which can be regarded as extensions of Kullmann's results for autark assignments of CNFs. As algorithmic results, we present a pseudo-polynomial time algorithm that computes a linear fixable assignment for a given integer linear system, which implies the well known pseudo-polynomial solvability for integer linear systems such as two-variable-per-inequality, Horn and q-Horn systems.

本文言語英語
ホスト出版物のタイトルProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
編集者Jerome Lang
出版社International Joint Conferences on Artificial Intelligence
ページ5622-5626
ページ数5
ISBN(電子版)9780999241127
出版ステータス出版済み - 2018
外部発表はい
イベント27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, スウェーデン
継続期間: 7 13 20187 19 2018

出版物シリーズ

名前IJCAI International Joint Conference on Artificial Intelligence
2018-July
ISSN(印刷版)1045-0823

その他

その他27th International Joint Conference on Artificial Intelligence, IJCAI 2018
国/地域スウェーデン
CityStockholm
Period7/13/187/19/18

All Science Journal Classification (ASJC) codes

  • 人工知能

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