Inference for a New Probabilistic Constraint Logic
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2540-2545.
Abstract
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. In this paper, we propose a new probabilistic constraint logic programming language, which combines constraint logic programming with probabilistic reasoning. The language supports modeling of discrete as well as continuous probability distributions by expressing constraints on random variables. We introduce the declarative semantics of this language, present an exact inference algorithm to derive bounds on the joint probability distributions consistent with the specified constraints, and give experimental results. The results obtained are encouraging, indicating that inference in our language is feasible for solving challenging problems.
@inproceedings{ijcai2013p374,
title = {Inference for a New Probabilistic Constraint Logic},
author = {Michels, Steffen and Hommersom, Arjen and Lucas, Peter J. F. and Velikova, Marina and Koopman, Pieter W. M.},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2540--2545},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/374},
}
TY - CPAPER TI - Inference for a New Probabilistic Constraint Logic AU - Michels, Steffen AU - Hommersom, Arjen AU - Lucas, Peter J. F. AU - Velikova, Marina AU - Koopman, Pieter W. M. T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2540 EP - 2545 UR - https://ijcai.org/proceedings/2013/374 ER -Copied
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