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Granular Computing and Human-Centricity in Computational Intelligence

Granular Computing and Human-Centricity in Computational Intelligence

Witold Pedrycz
Copyright: © 2010 |Volume: 2 |Issue: 4 |Pages: 16
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781613502648|DOI: 10.4018/jssci.2010100102
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MLA

Pedrycz, Witold. "Granular Computing and Human-Centricity in Computational Intelligence." IJSSCI vol.2, no.4 2010: pp.16-31. http://doi.org/10.4018/jssci.2010100102

APA

Pedrycz, W. (2010). Granular Computing and Human-Centricity in Computational Intelligence. International Journal of Software Science and Computational Intelligence (IJSSCI), 2(4), 16-31. http://doi.org/10.4018/jssci.2010100102

Chicago

Pedrycz, Witold. "Granular Computing and Human-Centricity in Computational Intelligence," International Journal of Software Science and Computational Intelligence (IJSSCI) 2, no.4: 16-31. http://doi.org/10.4018/jssci.2010100102

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Abstract

Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.

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