2017

Conférence

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), Hammamet, Tunisia, 2017, pp. 1450-1453, doi: 10.1109/AICCSA.2017.16.

Approximate reasoning aims to manage knowledge imprecision in the inference process. It is a generalization of the Modus Ponens of classical logic. Originally, it is defined in fuzzy logic context, where knowledge are modeled by a quantitative way. We are interested in this paper to approximate reasoning in the symbolic multi-valued logic context. This logic allows presenting imprecise knowledge in a qualitative way, where every predicate is modeled by a multi-set. In order to express imprecision, each multi-set is associated to a scale base of ordered symbolic degrees. In a previous work where a symbolic approximate reasoning has been defined, it has been assumed that all multi-sets of the inference schema have the same scale base. This has the disadvantage to prevent free definition of knowledge. For that, we propose in this paper a new approximate reasoning which can infer with multi-sets having different scale bases. Our solution consists of interfacing all the multi-sets in order to avoid information loss.

@inproceedings{kacem2017new,
  title={A New Approximate Reasoning for Multi-bases Symbolic Data},
  author={Kacem, Saoussen Bel Hadj},
  booktitle={2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)},
  pages={1450--1453},
  year={2017},
  organization={IEEE}
}