### Abstract

Hypothetical reasoning is a useful knowledge-processing framework applicable to many problems including system diagnosis, design, etc. However, due to its non-monotonic inference nature, it takes exponential computation-time to find a solution hypotheses-set to prove a given goal. This is also true for cost-based hypothetical reasoning to find an optimal solution with minimal cost. As for the hypothetical reasoning expressed in propositional logic, since it is easily transformed into 0-I integer programming problem, a polynomial-time method finding a near-optimal solution has been developed so far by employing an approximate solution method of 0-1 integer programming called the Pivot and Complement method. Also, by reforming this method, a network-based inference mechanism called Networked Bubble Propagation (NBP) has been invented by the authors, which allows even faster inference. More importantly, a network-based approach is meaningful, for its potential of being developed extending to a broader framework of knowledge processing. In this paper, we extend the NBP method to dealing with the hypothetical reasoning expressed with predicate logic. By constructing a series of knowledge networks, to which the NBP method is applied, in a stepwise manner according to a top-clown control, we avoid the excessive expansion of the network size. As a result, we can achieve a polynomial time inference for computing a hoax-optimal solution for the cost-based hypothetical reasoning in predicate-logic knowledge.

Original language | English |
---|---|

Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Publisher | Springer Verlag |

Pages | 375-387 |

Number of pages | 13 |

Volume | 1081 |

ISBN (Print) | 3540612912, 9783540612919 |

DOIs | |

Publication status | Published - 1996 |

Externally published | Yes |

Event | 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996 - Toronto, Canada Duration: 1996 May 21 → 1996 May 24 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|

Volume | 1081 |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996 |
---|---|

Country | Canada |

City | Toronto |

Period | 96/5/21 → 96/5/24 |

### Fingerprint

### Keywords

- Knowledge representation
- Reasoning (abduction)
- Search

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 1081, pp. 375-387). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1081). Springer Verlag. https://doi.org/10.1007/3-540-61291-2_66

**A polynomial-time predicate-logic hypothetical reasoning by networked bubble propagation method.** / Ohsawa, Yukio; Ishizuka, Mitsuru.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 1081, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1081, Springer Verlag, pp. 375-387, 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996, Toronto, Canada, 96/5/21. https://doi.org/10.1007/3-540-61291-2_66

}

TY - GEN

T1 - A polynomial-time predicate-logic hypothetical reasoning by networked bubble propagation method

AU - Ohsawa, Yukio

AU - Ishizuka, Mitsuru

PY - 1996

Y1 - 1996

N2 - Hypothetical reasoning is a useful knowledge-processing framework applicable to many problems including system diagnosis, design, etc. However, due to its non-monotonic inference nature, it takes exponential computation-time to find a solution hypotheses-set to prove a given goal. This is also true for cost-based hypothetical reasoning to find an optimal solution with minimal cost. As for the hypothetical reasoning expressed in propositional logic, since it is easily transformed into 0-I integer programming problem, a polynomial-time method finding a near-optimal solution has been developed so far by employing an approximate solution method of 0-1 integer programming called the Pivot and Complement method. Also, by reforming this method, a network-based inference mechanism called Networked Bubble Propagation (NBP) has been invented by the authors, which allows even faster inference. More importantly, a network-based approach is meaningful, for its potential of being developed extending to a broader framework of knowledge processing. In this paper, we extend the NBP method to dealing with the hypothetical reasoning expressed with predicate logic. By constructing a series of knowledge networks, to which the NBP method is applied, in a stepwise manner according to a top-clown control, we avoid the excessive expansion of the network size. As a result, we can achieve a polynomial time inference for computing a hoax-optimal solution for the cost-based hypothetical reasoning in predicate-logic knowledge.

AB - Hypothetical reasoning is a useful knowledge-processing framework applicable to many problems including system diagnosis, design, etc. However, due to its non-monotonic inference nature, it takes exponential computation-time to find a solution hypotheses-set to prove a given goal. This is also true for cost-based hypothetical reasoning to find an optimal solution with minimal cost. As for the hypothetical reasoning expressed in propositional logic, since it is easily transformed into 0-I integer programming problem, a polynomial-time method finding a near-optimal solution has been developed so far by employing an approximate solution method of 0-1 integer programming called the Pivot and Complement method. Also, by reforming this method, a network-based inference mechanism called Networked Bubble Propagation (NBP) has been invented by the authors, which allows even faster inference. More importantly, a network-based approach is meaningful, for its potential of being developed extending to a broader framework of knowledge processing. In this paper, we extend the NBP method to dealing with the hypothetical reasoning expressed with predicate logic. By constructing a series of knowledge networks, to which the NBP method is applied, in a stepwise manner according to a top-clown control, we avoid the excessive expansion of the network size. As a result, we can achieve a polynomial time inference for computing a hoax-optimal solution for the cost-based hypothetical reasoning in predicate-logic knowledge.

KW - Knowledge representation

KW - Reasoning (abduction)

KW - Search

UR - http://www.scopus.com/inward/record.url?scp=0042743350&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0042743350&partnerID=8YFLogxK

U2 - 10.1007/3-540-61291-2_66

DO - 10.1007/3-540-61291-2_66

M3 - Conference contribution

AN - SCOPUS:0042743350

SN - 3540612912

SN - 9783540612919

VL - 1081

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 375

EP - 387

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -