Representing Diagnostic Knowledge for Probabilistic Horn Abduction
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Qualitative Reasoning. Pages 1129-1136.
Abstract
This paper presents a simple logical framework for abduction , with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straight-forward implementations using branch and bound search with either logic-programming technology or ATMS technology. The main focus of this paper is arguing for a form of representational adequacy of this very simple system for diagnostic reasoning. It is shown how it can represent model-based knowledge, with and without faults, and with and without non-intermittency assumptions. It is also shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network.
@inproceedings{ijcai1991p166,
title = {Representing Diagnostic Knowledge for Probabilistic Horn Abduction},
author = {Poole, David},
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 = {1129--1136},
year = {1991},
month = {8},
note = {Qualitative Reasoning},
url = {https://ijcai.org/proceedings/1991/166},
}
TY - CPAPER TI - Representing Diagnostic Knowledge for Probabilistic Horn Abduction AU - Poole, David T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 1129 EP - 1136 UR - https://ijcai.org/proceedings/1991/166 ER -Copied
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