### 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.

元の言語 | English |
---|---|

ページ（範囲） | 369-376 |

ページ数 | 8 |

ジャーナル | Knowledge-Based Systems |

巻 | 15 |

発行部数 | 7 |

DOI | |

出版物ステータス | Published - 2002 9 1 |

外部発表 | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Artificial Intelligence

### これを引用

*Knowledge-Based Systems*,

*15*(7), 369-376. https://doi.org/10.1016/S0950-7051(02)00020-5

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

研究成果: Article

*Knowledge-Based Systems*, 巻. 15, 番号 7, pp. 369-376. https://doi.org/10.1016/S0950-7051(02)00020-5

}

TY - JOUR

T1 - SL method for computing a near-optimal solution using linear and non-linear programming in cost-based hypothetical reasoning

AU - Ishizuka, M.

AU - Matsuo, Y.

PY - 2002/9/1

Y1 - 2002/9/1

N2 - 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.

AB - 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.

KW - Hypothetical reasoning

KW - Linear programming

KW - Non-linear programming

UR - http://www.scopus.com/inward/record.url?scp=0036722349&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036722349&partnerID=8YFLogxK

U2 - 10.1016/S0950-7051(02)00020-5

DO - 10.1016/S0950-7051(02)00020-5

M3 - Article

AN - SCOPUS:0036722349

VL - 15

SP - 369

EP - 376

JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

SN - 0950-7051

IS - 7

ER -