@INPROCEEDINGS { Rsds2650,
author = {Liau, Churn-Jung}, 
title = {Ordered Belief Fusion in Possibilistic Logic}, 
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Part I}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 3641, 
pages = {632--641}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {August}, 
year = 2005, 
editor = {Slezak, Dominik and Wang, Guoying and Szczuka, Marcin and Duntsch, Ivo and Yao, Yiyu}, 
issn = {0302-9743}, 
isbn = {3-540-28653-5}, 
abstract = {In this paper, we propose a logical framework for reasoning about uncertain belief fusion. The framework is a combination of multi-agent epistemic logic and possibilistic logic. We use graded epistemic operators to represent agents' uncertain beliefs, and the operators are interpreted in accordance with possibilistic semantics. Ordered fusion can resolve the inconsistency caused by direct fusion. We consider two strategies to merge uncertain beliefs. In the first strategy, called level cutting fusion, if inconsistency occurs at some level, then all beliefs at the lower levels are discarded simultaneously. In the second, called level skipping fusion, only the level at which the inconsistency occurs is skipped. We present the formal semantics and axiomatic systems for these two strategies.}, 
keywords = {belief fusion, database merging, epistemic logic, multi-agent systems, possibilistic logic, }, 
}