Human Centricity and Perception-Based Perspective and Their Centrality to the Agenda of Granular Computing

Human Centricity and Perception-Based Perspective and Their Centrality to the Agenda of Granular Computing

Witold Pedrycz (University of Alberta, Canada and Polish Academy of Sciences, Poland)
DOI: 10.4018/jcini.2011100104
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Abstract

In spite of their striking diversity, numerous tasks and architectures of intelligent systems such as those permeating multivariable data analysis, decision-making processes along with their underlying models, recommender systems and others exhibit two evident commonalities. They promote (a) human centricity and (b) vigorously engage perceptions (rather than plain numeric entities) in the realization of the systems and their further usage. Information granules play a pivotal role in such settings. Granular Computing delivers a cohesive framework supporting a formation of information granules and facilitating their processing. The author exploits two essential concepts of Granular Computing. The first one deals with the construction of information granules. The second one helps endow constructs of intelligent systems with a much needed conceptual and modeling flexibility. The study elaborates in detail on the three representative studies. In the first study being focused on the Analytic Hierarchy Process (AHP) used in decision-making, the author shows how an optimal allocation of granularity helps improve the quality of the solution and facilitate collaborative activities in models of group decision-making. The second study is concerned with a granular interpretation of temporal data where the role of information granularity is profoundly visible when effectively supporting human centric description of relationships existing in data. The third study concerns a formation of granular logic descriptors on a basis of a family of logic descriptors.
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2. The Principle Of Justifiable Granularity

Here we are concerned with the formation of a single information granule Ω based on some experimental evidence being a set of a single-dimensional (scalar) numeric data, D = {x1, x2, …, xN}.The information granule itself could be expressed in a certain formal framework of Granular Computing. The principle of justifiable granularity (Pedrycz & Gomide, 2007) is concerned with a formation of a meaningful information granule based on available experimental evidence. In its formation, such a construct has to adhere to the two intuitively compelling requirements:

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