@INPROCEEDINGS { Rsds3263,
author = {Skowron, Andrzej}, 
title = {Toward Intelligent Systems: Calculi of Information Granules}, 
booktitle = {Proceedings of International Workshop on Rough Set Theory and Granular Computing (RSTGC-2001)}, 
conference = {International Workshop on Rough Set Theory and Granular Computing}, 
series =  = {Bulletin of International Rough Set Society}, 
volume = 5, 
pages = {9--30}, 
month = {May}, 
year = 2001, 
editor = {Hirano, Shoji and Inuiguchi, Masahiro and Tsumoto, Shusaku}, 
issn = {1346-0013}, 
abstract = {Information sources provide us with granules of information that must be transformed, analyzed and built into structures that support problem solving. Lotfi A. Zadeh has recently pointed out to the need to develop a new research branch called Computing with Words. One way to achieve Computing with Words is trough Granular Computing (GC). The main concepts of GC are related to information granule calculi. One of the main goals of information granule calculi is to develop algorithmic methods for construction of complex information granules from elementary ones by means of available operations and inclusion (closeness) measures. These constructions can also be interpreted as approximate schemes of reasoning (AR-schemes). These constructed complex granules represent a form of information fusion. Such constructed granules should satisfy some constraints like quality criteria or/and degrees of granule inclusion in (closeness to) a given information granule. In the paper we discuss the idea of the rough neurocomputing paradigm for inducing AR-schemes based on rough sets and, in particular, on rough mereology. Information granule decomposition methods are important components of methods for AR-schemes induced from data and background knowledge. We discuss some information granule decomposition methods.}, 
keywords = {rough sets, rough mereology, information granulation, pattern, granular computing, schemes of approximate reasoning, decomposition, information fusion, rough neuron, rough neurocomputing, }, 
}