ML Anthology

Scalability Study of Peer-to-Peer Consequence Finding

Philippe Adjiman, Philippe Chatalic, François Goasdoué, Marie-Christine Rousset, Laurent Simon
IJCAI 2005 pp. 351-356
/ijcai/2005/adjiman2005ijcai-scalability/

Abstract

In peer-to-peer inference systems, each peer can reason locally but also solicit some of its acquaintances, sharing part of its vocabulary. This paper studies both theoretically and experimentally the problem of computing proper prime implicates for propositional peer-to-peer systems, the global theory (union of all peer theories) of which is not known (as opposed to partition-based reasoning). 1

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Text

Adjiman et al. "Scalability Study of Peer-to-Peer Consequence Finding." International Joint Conference on Artificial Intelligence, 2005.

Markdown

[Adjiman et al. "Scalability Study of Peer-to-Peer Consequence Finding." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/adjiman2005ijcai-scalability/)

BibTeX

@inproceedings{adjiman2005ijcai-scalability,
  title     = {{Scalability Study of Peer-to-Peer Consequence Finding}},
  author    = {Adjiman, Philippe and Chatalic, Philippe and Goasdoué, François and Rousset, Marie-Christine and Simon, Laurent},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {351-356},
  url       = {https://mlanthology.org/ijcai/2005/adjiman2005ijcai-scalability/}
}