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ICLP

2009

Springer

2009

Springer

Probabilistic logic programs (PLPs) define a set of probability distribution functions (PDFs) over the set of all Herbrand interpretations of the underlying logical language. When answering a query Q, a lower and upper bound on Q is obtained by optimizing (min and max) an objective function subject to a set of linear constraints whose solutions are the PDFs mentioned above. A common critique not only of PLPs but many probabilistic logics is that the difference between the upper bound and lower bound is large, thus often providing very little useful information in the query answer. In this paper, we provide a new method to answer probabilistic queries that tries to come up with a histogram that "maps" the probability that the objective function will have a value in a given interval, subject to the above linear constraints. This allows the system to return to the user a histogram where he can directly "see" what the most likely probability range for his query will be...

Related Content

Added |
22 Nov 2009 |

Updated |
22 Nov 2009 |

Type |
Conference |

Year |
2009 |

Where |
ICLP |

Authors |
Matthias Broecheler, Gerardo I. Simari, V. S. Subrahmanian |

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