Toward a Theory of Granular Computing for Human-Centered Information Processing

Bargieła, Andrzej; Pedrycz, Witold

doi:10.1109/tfuzz.2007.905912

Cited by 243 publications

(72 citation statements)

References 23 publications

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“…Granular computing has emerged as one of the fastest growing intelligent computing paradigms in the domain of cognitive intelligence and artificial intelligence (Yao et al 2013). It is often loosely regarded as an umbrella term to cover theories, methodologies, techniques, and tools that make use of granules in complex problem solving (Zadeh 1997(Zadeh , 2007Pedrycz 2001Pedrycz , 2006Lin 2003;Yao 2004Yao , 2005Bargiela and Pedrycz 2008). Granular computing could be adopted into a structured combination of algorithmic and non-algorithmic information processing that mimics humans by intelligent synthesis of knowledge from information.…”

Section: Cognitive Computing: Brain/mind Inspired Computingmentioning

confidence: 99%

“…Granular computing could be adopted into a structured combination of algorithmic and non-algorithmic information processing that mimics humans by intelligent synthesis of knowledge from information. Bargiela and Pedrycz consider granular computing as a conceptual and algorithmic platform for analyzing and designing human-centric intelligent systems (Bargiela and Pedrycz 2008). Zadeh considers granular computing as a basis for computing with words which computes with information described in natural language (Zadeh 1996).…”

Section: Cognitive Computing: Brain/mind Inspired Computingmentioning

confidence: 99%

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DGCC: data-driven granular cognitive computing

Wang

2017

Granul. Comput.

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2016 is the 60 anniversary of the birth of artificial intelligence (AI). In the past 60 years, many AI theoretical models have been proposed and great achievements have been accomplished. Most intelligent computing models are inspired by various human/ natural/social intelligence mechanisms. Three main schools of artificial intelligence are formed, that is, symbolism, connectionism and behaviorism. Artificial intelligence is intelligence exhibited by machines. It is applied when a machine mimics cognitive functions that humans associate with other human minds. Achievements of cognitive science could give much inspiration to AI. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind's capabilities. It is one of the most critical tasks for artificial intelligence researchers to develop advanced cognitive computing models. Cognitive computing is the third and most transformational phase in computing's evolution, after the two distinct eras of computing-the tabulating era and the programming era. Inspired by human's granularity thinking, problem solving mechanism and the cognition law of ''global precedence'', a new powerful cognitive computing model, data-driven granular cognitive computing (DGCC), is proposed in this paper. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human's cognition mechanism of ''global precedence'' which is a cognition process of ''from coarser to finer'' and the information processing mechanism of machine learning systems which is ''from finer to coarser'', in a multiple granularity space. It is also based on the idea of data-driven. The research issues of DGCC to be further addressed are discussed. Based on DGCC, deep learning is neither classified into symbolism, nor connectionism. It is taken as a combination of symbolism and connectionism, and named hierarchical structuralism in this paper. The HD 3 characteristics (hierarchical, distributed, data-driven, and dynamical) of the hierarchical structuralism are analyzed. DGCC provides a granular cognitive computing framework for efficient knowledge discovery from big data.

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Section: Cognitive Computing: Brain/mind Inspired Computingmentioning

confidence: 99%

Section: Cognitive Computing: Brain/mind Inspired Computingmentioning

confidence: 99%

DGCC: data-driven granular cognitive computing

Wang

2017

Granul. Comput.

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“…Granular computing (Pedrycz and Chen 2014;Pedrycz 2013;Han and Lin 2010;Bargiela and Pedrycz 2008) is typically portrayed as a research context intended as a convergence of various modeling and computational approaches for dealing with uncertainty. The modeling side is essentially rooted on formal constructs called information granules (IG) .…”

Section: Introductionmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the peculiar aspects related to the analysis of non-geometric patterns, which have recently attracted considerable attention by researchers. As a conclusion, we elaborate over the fundamental, conceptual problems underlying the process of data granulation, which drive the quest for a sound theory of granular computing.

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“…We used the domain transformation method to approach the problem of cost effectiveness in scheduling nurses. This approach was introduced practically by Baskaran et al (2013) to demonstrate the information granulation methodology (Bargiela et al, 2002(Bargiela et al, , 2008 for the generation of multiple feasible lowcost rosters. These rosters were then evaluated.…”

Section: Introductionmentioning

confidence: 99%