Model-Based Diagnosis using Causal Networks

Darwiche, Adnan

Model-Based Diagnosis using Causal Networks

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Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Automated Reasoning. Pages 211-218.

Abstract

This paper rests on several contributions. First , we introduce the notion of a consequence, which is a boolean expression that characterizes consistency-based diagnoses. Second, we introduce a basic algorithm for computing consequences when the system description is structured using a causa l network. We show that if the ca usal network has no undirected cycles, then a consequence has a linear size and can be computed in linear time. Fin ally, we show that diagnoses characterized by a consequence and meeting some preference criterion can be extracted from the consequence in time linear in its size. A dual set of results is provided for abductive diagnosis.

Adnan Darwiche. 1995. Model-Based Diagnosis using Causal Networks. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95. IJCAI, 211–218. https://ijcai.org/proceedings/1995/28

@inproceedings{ijcai1995p28,
  title     = {Model-Based Diagnosis using Causal Networks},
  author    = {Darwiche, Adnan},
  booktitle = {Proceedings of the Fourteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-95}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Chris S. Mellish},
  pages     = {211--218},
  year      = {1995},
  note      = {Automated Reasoning},
  url       = {https://ijcai.org/proceedings/1995/28},
}

TY  - CPAPER
TI  - Model-Based Diagnosis using Causal Networks
AU  - Darwiche, Adnan
T2  - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95
PB  - International Joint Conferences on Artificial Intelligence
PY  - 1995
SP  - 211
EP  - 218
UR  - https://ijcai.org/proceedings/1995/28
ER  -