Partial first-order logic relying on optimistic, pessimistic and average partial membership functions

DOI

10.2991/eusflat.2013.53How to use a DOI?

Keywords

Approximation of sets rough sets decision-theoretic rough sets partial first-order logic

Abstract

One of the common features of decision-theoretic rough set models is that they rely on total background (available) knowledge in the sense that the knowledge covers the discourse universe. In the proposed framework the author gives up this requirement and allows that available knowledge about the discourse universe may be partial. It is shown by introducing optimistic, average and pessimistic partial membership functions that a decision--theoretic rough set model can be based on a very general version of partial approximation spaces. Different membership functions may serve as a base of the semantics of a partial first--order logic. The proposed logical system gives an exact possibility to introduce different semantic notions of logical consequence relations which can be used in order to make clear the consequences of our decisions.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tamás Mihálydeák
PY  - 2013/08
DA  - 2013/08
TI  - Partial first-order logic relying on optimistic, pessimistic and average partial membership functions
BT  - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
PB  - Atlantis Press
SP  - 374
EP  - 379
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2013.53
DO  - 10.2991/eusflat.2013.53
ID  - Mihálydeák2013/08
ER  -