## Abstract

A generalization of logic program rules is proposed where rules are built from weight constraints with type information for each predicate instead of simple literals. These kinds of constraints are useful for concisely representing different kinds of choices as well as cardinality, cost and resource constraints in combinatorial problems such as product configuration. A declarative semantics for the rules is presented which generalizes the stable model semantics of normal logic programs. It is shown that for ground rules the complexity of the relevant decision problems stays in NP. The first implementation of the language handles a decidable:subset where function symbols are not allowed. It is based on a new procedure for computing stable models for ground rules extending normal programs with choice and weight constructs and a compilation technique where a weight rule with variables is transformed to a set of such simpler ground rules.

Original language | English |
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Title of host publication | Logic Programming and Nonmonotonic Reasoning |

Subtitle of host publication | 5th International Conference, LPNMR’ 99 El Paso, Texas, USA, December 2–4, 1999. Proceedings |

Editors | M. Gelfond, N. Leone, G. Pfeifer |

Place of Publication | Berlin |

Pages | 317-331 |

Number of pages | 15 |

ISBN (Electronic) | 978-3-540-46767-0 |

DOIs | |

Publication status | Published - 1999 |

MoE publication type | A4 Article in a conference publication |

Event | International Conference on Logic Programming and Nonmonotonic Reasoning - El Paso, United States Duration: 2 Dec 1999 → 4 Dec 1999 Conference number: 5 |

### Publication series

Name | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE |
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Publisher | SPRINGER-VERLAG BERLIN |

Volume | 1730 |

ISSN (Print) | 0302-9743 |

### Conference

Conference | International Conference on Logic Programming and Nonmonotonic Reasoning |
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Abbreviated title | LPNMR |

Country/Territory | United States |

City | El Paso |

Period | 02/12/1999 → 04/12/1999 |

## Keywords

- linear inequalities
- logic programs
- stable model semantics