Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 243-248.
Abstract
P-log is a probabilistic logic programming language, which combines both logic programming style knowledge representation and probabilistic reasoning. In earlier papers various advantages of P-log have been discussed. In this paper we further elaborate on the KR prowess of P-log by showing that: (i) it can be used for causal and counterfactual reasoning and (ii) it provides an elaboration tolerant way for non-naive conditioning.
Chitta Baral, and Matt Hunsaker. 2007. Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 243–248. https://ijcai.org/proceedings/2007/37
Baral, C., & Hunsaker, M. (2007). Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 (pp. 243–248). IJCAI. https://ijcai.org/proceedings/2007/37
@inproceedings{ijcai2007p37,
title = {Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning},
author = {Baral, Chitta and Hunsaker, Matt},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {243--248},
year = {2007},
url = {https://ijcai.org/proceedings/2007/37},
}
TY - CPAPER TI - Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning AU - Baral, Chitta AU - Hunsaker, Matt T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 243 EP - 248 UR - https://ijcai.org/proceedings/2007/37 ER -Copied
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