Abstract
Granular computing is concerned with processing carried out at a level of coherent conceptual entities - information granules. Such granules are viewed as inherently conceptual entities formed at some level of abstraction whose processing is rooted in the language of logic (especially, many valued or fuzzy logic). The objective of this study is to discuss the role of Granular Computing and logic processing in the context of intelligent systems and demonstrate that both of them are organized into a single conceptual and computational framework. We provide a general overview of Granular Computing emphasizing a diversity of the currently available concepts and underlining their common features that make the entire pursuit highly coherent. The logic facet of processing is embodied in the framework of fuzzy logic and fuzzy sets that constitutes a consistent background necessary for representing and processing information granules. Several main categories of logic processing units (logic neurons) are discussed that support aggregative (and-like and or-like operators) and referential logic mechanisms (dominance, inclusion, and matching). We show how the logic neurons contribute to high functional transparency of granular processing, help capture prior domain knowledge and give rise to a diversity of the resulting models.