MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings
Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Knowledge Representation. Pages 366-371.
Abstract
Experience gained through building a causal network for interpretation of electromyographic findings has shown that probabilistic inference is a realistic possibility in networks of non-trivial size. The use of nodes with many internal states has made it possible to make a conceptually simple and compact representation of knowledge. "Deep knowledge" in the form of pathophysiological models are used to reduce the problem of estimating thousands of conditional probabilities to a rnanageble size. The network has built-in mechanisms that will detect when the network is confronted with a situation outside the limits of its own knowledge and it handles conflicting evidence in a simple and consistent way.
@inproceedings{ijcai1987p76,
title = {MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings},
author = {Andreassen, Steen and Woldbye, Marianne and Falck, Björn and Andersen, Stig K.},
booktitle = {Proceedings of the Tenth International Joint Conference on
Artificial Intelligence, {IJCAI-87}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John McDermott},
pages = {366--371},
year = {1987},
note = {Knowledge Representation},
url = {https://ijcai.org/proceedings/1987/76},
}
TY - CPAPER TI - MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings AU - Andreassen, Steen AU - Woldbye, Marianne AU - Falck, Björn AU - Andersen, Stig K. T2 - Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI-87 PB - International Joint Conferences on Artificial Intelligence PY - 1987 SP - 366 EP - 371 UR - https://ijcai.org/proceedings/1987/76 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: