Inference method for fuzzy quantified and truth qualified natural language propositions
✍ Scribed by Wataru Okamoto; Shun'ichi Tano; Atsushi Inoue; Ryosuke Fujioka
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 509 KB
- Volume
- 83
- Category
- Article
- ISSN
- 1042-0967
No coin nor oath required. For personal study only.
✦ Synopsis
This paper proposes an inference method for fuzzy quantified and truth qualified natural language propositions. For example, for Most tall men are heavy is true, a modified proposition Many more or less tall men are heavy is true, can be derived by inference. For the quantifier more or less, the fuzzy quantifier many can be derived analytically. Three types of fuzzy quantifiers (the monotonically nonincreasing type such as few, the monotonically nondecreasing type such as most, and the single-peaked type such as several), as well as the monotonic and injection type truth quantified qualifier such as true and false, are considered. For a proposition containing those quantifiers/qualifiers, it is shown that the fuzzy quantifier can be derived analytically by the fuzzy inference, for the modification to quantify the fuzzy subject part.