Name
Abstract | ||
|---|---|---|
In this paper, we propose a granularity-based framework of deduction, induction, and abduction using variable precision rough set models proposed by Ziarko and measure-based semantics for modal logic proposed by Murai et al. The proposed framework is based on @a-level fuzzy measure models on the basis of background knowledge, as described in the paper. In the proposed framework, deduction, induction, and abduction are characterized as reasoning processes based on typical situations about the facts and rules used in these processes. Using variable precision rough set models, we consider @b-lower approximation of truth sets of nonmodal sentences as typical situations of the given facts and rules, instead of the truth sets of the sentences as correct representations of the facts and rules. Moreover, we represent deduction, induction, and abduction as relationships between typical situations. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.ijar.2009.06.002 | Int. J. Approx. Reasoning |
Keywords | Field | DocType |
abduction,truth set,typical situation,variable precision rough sets,induction,deduction,correct representation,proposed framework,granularity-based framework,a-level fuzzy measure model,modal logic,rough set model,variable precision,b-lower approximation,rough set | Variable precision,Fuzzy logic,Phrase,Rough set,Modal logic,Artificial intelligence,Granularity,Sentence,Machine learning,Mathematics,Semantics | Journal |
Volume | Issue | ISSN |
50 | 8 | International Journal of Approximate Reasoning |
Citations | PageRank | References |
4 | 0.40 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yasuo Kudo | 1 | 95 | 26.41 |
| Tetsuya Murai | 2 | 186 | 42.10 |
| Seiki Akama | 3 | 71 | 27.71 |