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

M. Ishizuka, Y. Matsuo

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)369-376
Number of pages8
JournalKnowledge-Based Systems
Volume15
Issue number7
DOIs
Publication statusPublished - 2002 Sep 1
Externally publishedYes

Fingerprint

Knowledge based systems
Nonlinear programming
Linear programming
Costs
Optimal solution
Inference

Keywords

  • Hypothetical reasoning
  • Linear programming
  • Non-linear programming

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

SL method for computing a near-optimal solution using linear and non-linear programming in cost-based hypothetical reasoning. / Ishizuka, M.; Matsuo, Y.

In: Knowledge-Based Systems, Vol. 15, No. 7, 01.09.2002, p. 369-376.

Research output: Contribution to journalArticle

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