Efficient Consequence Finding

Simon, Laurent; del Val, Alvaro

Efficient Consequence Finding

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Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. Pages 359-369.

Abstract

We present an extensive experimental study of consequence-finding algorithms based on kernel resolution, using both a trie-based and a novel ZBDD-based implementation, which uses ZeroSuppressed Binary Decision Diagrams to concisely store and process very large clause sets. Our study considers both the full prime implicate task and applications of consequence-finding for restricted target languages in abduction, model-based and faulttree diagnosis, and polynomially-bounded knowledge compilation. We show that the ZBDD implementation can push consequence-finding to a new limit, solving problems which generate over 1070 clauses.

Laurent Simon, and Alvaro del Val. 2001. Efficient Consequence Finding. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01. IJCAI, 359–369. https://ijcai.org/proceedings/2001/51

@inproceedings{ijcai2001p51,
  title     = {Efficient Consequence Finding},
  author    = {Simon, Laurent and del Val, Alvaro},
  booktitle = {Proceedings of the Seventeenth International Joint Conference on
               Artificial Intelligence, {IJCAI-01}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  pages     = {359--369},
  year      = {2001},
  url       = {https://ijcai.org/proceedings/2001/51},
}

TY  - CPAPER
TI  - Efficient Consequence Finding
AU  - Simon, Laurent
AU  - del Val, Alvaro
T2  - Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2001
SP  - 359
EP  - 369
UR  - https://ijcai.org/proceedings/2001/51
ER  -