First-Order Model Counting in a Nutshell
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4086-4088.
Abstract
First-order model counting recently emerged as a computational tool for high-level probabilistic reasoning. It is concerned with counting satisfying assignments to sentences in first-order logic and upgrades the successful propositional model counting approaches to probabilistic reasoning. We give an overview of model counting as it is applied in statistical relational learning, probabilistic programming, databases, and hybrid reasoning. A short tutorial illustrates the principles behind these solvers. Finally, we show that first-order counting is a fundamentally different problem from the propositional counting techniques that inspired it.
@inproceedings{ijcai2016p607,
title = {First-Order Model Counting in a Nutshell},
author = {Van den Broeck, Guy},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {4086--4088},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/607},
}
TY - CPAPER TI - First-Order Model Counting in a Nutshell AU - Van den Broeck, Guy T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 4086 EP - 4088 UR - https://ijcai.org/proceedings/2016/607 ER -Copied
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