A Computational Model for Causal and Diagnostic Reasoning in Inference Systems
Proceedings of the Eighth International Joint Conference on Artificial Intelligence. Expert Systems. Pages 190-192.
Abstract
This paper introdu c es a representation of ev id e nti a l relationships which permits updating of be lief in tw o simultaneous modes: causal (i . e. top-down ) Rnd diagn o stic (i.e. bottom-up). It extends t he hierarchical tree representation by all owing multiple causes to a given manifestation. We develop an updating scheme that obeys the axioms of probability, is co mputationally efficient, and is compatible with experts reasoning. The belief parameters of each variable are defined and updated by those of its neighbors in such a way that the impact of each new e vidence propagates and settles through the network in a single pass.
@inproceedings{ijcai1983p45,
title = {A Computational Model for Causal and Diagnostic Reasoning in Inference Systems},
author = {Kim, Jin H. and Pearl, Judea},
booktitle = {Proceedings of the Eighth International Joint Conference on
Artificial Intelligence, {IJCAI-83}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Alan Bundy},
pages = {190--192},
year = {1983},
note = {Expert Systems},
url = {https://ijcai.org/proceedings/1983/45},
}
TY - CPAPER TI - A Computational Model for Causal and Diagnostic Reasoning in Inference Systems AU - Kim, Jin H. AU - Pearl, Judea T2 - Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI-83 PB - International Joint Conferences on Artificial Intelligence PY - 1983 SP - 190 EP - 192 UR - https://ijcai.org/proceedings/1983/45 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
