A triarchic theory of granular computing

Yao, Yiyu

doi:10.1007/s41066-015-0011-0

Cited by 219 publications

(49 citation statements)

References 66 publications

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“…4 (Yao 2016b), Yao views granular computing as a complementary and dependent triangle, which integrates three important perspectives, namely, philosophy of structured thinking, methodology of structured problem solving, and mechanism of structured information processing (Yao 2016b). He proposed a trisecting-and-acting model to explain three way decisions (3WD), a class of effective ways and heuristics commonly used in human problem solving and information processing, in terms of two basic tasks.…”

Section: Cognitive Computing: Brain/mind Inspired Computingmentioning

confidence: 99%

“…4 The granular computing triangle (Yao 2016b) 2014; Li et al 1995). It uses three parameters, namely expected value, entropy and hyper entropy, to describe the intension of a concept, while a set of samples to describe its corresponding extension.…”

Section: Cognitive Computing: Brain/mind Inspired Computingmentioning

confidence: 99%

“…There have already been some studies about bottom-up and top-down processing in psychology and cognitive science (Lindsay and Norman 1977). Yao studied three top-down approaches, bottom-up approaches, and middle-out approaches (Yao 2016b). In DGCC, two kinds of transformation operators, namely, upward operators and downward operators are needed to simulate and implement such human intelligence mechanisms.…”

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%

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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“…Such higher-level classifications were useful to limit the amounts of compressed data packs required to access to finish calculations. Actually, one could think about it as production-ready example of a granular data analytics mechanism switching efficiently between the detailed and summary-based data representations -a mechanism envisioned in a number of studies on the principles of granular computing (Yao 2016).…”

Section: Historical Backgroundmentioning

confidence: 99%

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

Ślęzak

Glick

Betliński³

et al. 2017

J Intell Inf Syst

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We outline the processes of intelligent creation and utilization of granulated data summaries in the engine aimed at fast approximate execution of analytical SQL statements. We discuss how to use the introduced engine for the purposes of ad-hoc data exploration over large and quickly increasing data collected in a heterogeneous or distributed fashion. We focus on mechanisms that transform input data summaries into result sets representing query outcomes. We also illustrate how our computational principles can be put together with other paradigms of scaling and harnessing data analytics.

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“…In the light of extensive results from many fields (Yao 2016) that use various mechanisms of Grc, it is important to construct conceptual reviews to support granular computing to better solve problems. Generally speaking, traditional granular computing mechanisms solve problems by serial computing methods on a single granularity layer (that means computed on a single granularity layer each time).…”

Section: Introductionmentioning

confidence: 99%

Granular computing: from granularity optimization to multi-granularity joint problem solving

2016

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Human beings solve problems in different granularity worlds and shift from one granularity world to another quickly. It reflects human beings' intelligence in problem solving to some extent. In the era of big data, some new problems are emerging in real life. For example, traditional big data processing models always compute from raw data, failing to consider the granularity feature of human. Thus, they are hard to solve the 3 V characteristics of big data. Granular computing (GrC) combines the multigranularity thinking pattern of human intelligence with problem solving mode to deal with big data. Based on the related notions and characteristics of GrC, this paper reviews the previous studies of GrC in three progressive levels: granularity optimization, granularity conversion and multi-granularity joint problem solving. Then we proposed the diagram for relationship among three basic modes of GrC. Furthermore, the feasibility of GrC for big data processing is analyzed. Some research prospects of granular computing are given.

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A triarchic theory of granular computing

Cited by 219 publications

References 66 publications

DGCC: data-driven granular cognitive computing

DGCC: data-driven granular cognitive computing

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

Granular computing: from granularity optimization to multi-granularity joint problem solving

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