ML Anthology

A Measure of Closeness of Weak Implication to Strict Implication

Masanori B. Okamoto
IJCAI 1979 pp. 696-698
/ijcai/1979/okamoto1979ijcai-measure/

Abstract

The concept of weak implication introduced by R. Boudon is relevant to approximate reasoning which is a subject of the study in AI. The weak implication can be interpreted by the fuzzy material implication which is a binary fuzzy relation. A measure of closeness of weak implication to strict implication is defined as the maximum of distances within the given fuzzy relation, but constructed in the 2×2 contigency table. This measure agrees with the absolute value of Boudon's index f of closeness of weak implication to strict implication under some conditions.

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Text

Okamoto. "A Measure of Closeness of Weak Implication to Strict Implication." International Joint Conference on Artificial Intelligence, 1979.

Markdown

[Okamoto. "A Measure of Closeness of Weak Implication to Strict Implication." International Joint Conference on Artificial Intelligence, 1979.](https://mlanthology.org/ijcai/1979/okamoto1979ijcai-measure/)

BibTeX

@inproceedings{okamoto1979ijcai-measure,
  title     = {{A Measure of Closeness of Weak Implication to Strict Implication}},
  author    = {Okamoto, Masanori B.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {1979},
  pages     = {696-698},
  url       = {https://mlanthology.org/ijcai/1979/okamoto1979ijcai-measure/}
}