Causal interaction: from a high-level representation to an operational event-based representation
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. Pages 565-571.
Abstract
We propose to extend the temporal causal graph formalisms used in model-based diagnosis in order to deal with non trivial interactions like (partial) cancellation of fault effects. A high-level causal language is defined in which properties such as the persistence of effects and the triggering or sustaining properties of causes can be expressed. Various interaction phenomena are associated with these features. Instead of proposing an ad hoc reasoning mechanism to process this extended formalism, the specifications in this language are automatically translated into an event calculus based language having well-established semantics. Our approach improves the way fault interaction and intermittent faults are coped with in temporal abductive diagnosis.
@inproceedings{ijcai2001p79,
title = {Causal interaction: from a high-level representation to an operational event-based representation},
author = {Grosclaude, Irène and Cordier, Marie-Odile and Quiniou, Rene},
booktitle = {Proceedings of the Seventeenth International Joint Conference on
Artificial Intelligence, {IJCAI-01}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {565--571},
year = {2001},
url = {https://ijcai.org/proceedings/2001/79},
}
TY - CPAPER TI - Causal interaction: from a high-level representation to an operational event-based representation AU - Grosclaude, Irène AU - Cordier, Marie-Odile AU - Quiniou, Rene T2 - Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01 PB - International Joint Conferences on Artificial Intelligence PY - 2001 SP - 565 EP - 571 UR - https://ijcai.org/proceedings/2001/79 ER -Copied
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