Uncertainty in Artificial Intelligence
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Loglinear models for first-order probabilistic reasoning
James Cussens
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, for 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.
Keywords: loglinear models, constraint logic programming, inductive logic programming
Pages: 126-133
PS Link: ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/uai99.ps.gz
PDF Link: /papers/99/p126-cussens.pdf
BibTex:
@INPROCEEDINGS{Cussens99,
AUTHOR = "James Cussens ",
TITLE = "Loglinear models for first-order probabilistic reasoning",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "126--133"
}


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