SL method for computing a near-optimal solution using linear and non-linear programming in cost-based hypothetical reasoning

M. Ishizuka*, Y. Matsuo

*この研究の対応する著者

研究成果: Article査読

5 被引用数 (Scopus)

抄録

Hypothetical reasoning is an important framework for knowledge-based systems, however, its inference time grows exponentially with respect to problem size. In this paper, we present an understandable efficient method called slide-down and lift-up (SL) method which uses a linear programming technique for determining an initial search point and a non-linear programming technique for efficiently finding a near-optimal 0-1 solution. To escape from trapping into local optima, we have developed a new local handler, which systematically fixes a variable to a locally consistent value. Since the behavior of the SL method is illustrated visually, the simple inference mechanism of the method can be easily understood.

本文言語English
ページ(範囲)369-376
ページ数8
ジャーナルKnowledge-Based Systems
15
7
DOI
出版ステータスPublished - 2002 9月 1
外部発表はい

ASJC Scopus subject areas

  • 人工知能

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