Directed Constraint Networks: A Relational Framework for Causal Modeling
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Qualitative Reasoning. Pages 1164-1169.
Abstract
Normally, constraint networks are undirected, since constraints merely tell us which sets of values are compatible, and compatibility is a symmetrical relationship. In contrast, causal models use directed links, conveying cause-effect asymmetries. In this paper we give a relational semantics to this directionality, thus explaining why prediction is easy while diagnosis and planning are hard. We use this semantics to show that certain relations possess intrinsic directionalities, similar to those characterizing causal influences. We also use this semantics to decide when and how an unstructured set of symmetrical constraints can be configured so as to form a directed causal theory
@inproceedings{ijcai1991p171,
title = {Directed Constraint Networks: A Relational Framework for Causal Modeling},
author = {Dechter, Rina and Pearl, Judea},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {1164--1169},
year = {1991},
month = {8},
note = {Qualitative Reasoning},
url = {https://ijcai.org/proceedings/1991/171},
}
TY - CPAPER TI - Directed Constraint Networks: A Relational Framework for Causal Modeling AU - Dechter, Rina AU - Pearl, Judea T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 1164 EP - 1169 UR - https://ijcai.org/proceedings/1991/171 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
