## Abstract

Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities. We have a conservative extension of first-order reasoning, so that, far example, there is a one-one mapping between logical and random variables. We show how, in this framework, Inductive Logic Programming (ILP) can be used to induce the features of a loglinear model from data. We also compare the presented framework with other approaches to first-order probabilistic reasoning.

Original language | English |
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Title of host publication | UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS |

Editors | KB Laskey, H Prade |

Place of Publication | SAN FRANCISCO |

Publisher | MORGAN KAUFMANN PUB INC |

Pages | 126-133 |

Number of pages | 8 |

ISBN (Print) | 1-55860-614-9 |

Publication status | Published - 1999 |

Event | 15th Conference on Uncertainty in Artificial Intelligence - STOCKHOLM Duration: 30 Jul 1999 → 1 Aug 1999 |

### Conference

Conference | 15th Conference on Uncertainty in Artificial Intelligence |
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City | STOCKHOLM |

Period | 30/07/99 → 1/08/99 |

## Keywords

- loglinear models
- constraint logic programming
- inductive logic programming