Granular Computing and Human-Centricity in Computational Intelligence

2010 ◽  
Vol 2 (4) ◽  
pp. 16-31 ◽  
Author(s):  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Granular Computing ◽  
Knowledge Generation ◽  
Qualitative Assessment ◽  
Information Granules ◽  
Knowledge Based ◽  
Performance Indexes ◽  
Numeric Data ◽  
Type Information

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.

Granular Computing and Human-Centricity in Computational Intelligence

2012 ◽  
pp. 13-27
Author(s):  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Granular Computing ◽  
Knowledge Generation ◽  
Qualitative Assessment ◽  
Information Granules ◽  
Knowledge Based ◽  
Performance Indexes ◽  
Numeric Data ◽  
Type Information

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.

Granular Computing and Human-Centricity in Computational Intelligence

2012 ◽  
pp. 1721-1735
Author(s):  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Granular Computing ◽  
Knowledge Generation ◽  
Qualitative Assessment ◽  
Information Granules ◽  
Knowledge Based ◽  
Performance Indexes ◽  
Numeric Data ◽  
Type Information

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.

Innovative performance of project teams: the role of organizational structure and knowledge-based dynamic capability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rodrigo Valio Dominguez Gonzalez
Keyword(s):  
Organizational Structure ◽  
Industrial Sector ◽  
Project Team ◽  
Project Teams ◽  
Knowledge Generation ◽  
Dynamic Capability ◽  
Content Type ◽  
Innovative Performance ◽  
Knowledge Based ◽  
The Impact

Purpose This study aims to investigate the relationship between knowledge-based dynamic capability and organizational structure on team innovative performance in Brazilian industrial companies. Design/methodology/approach This study is based on data from a survey of 262 respondents from 65 companies in the Brazilian industrial sector with project teams and followed the partial least squares approach to model the structural equation that was used for data analysis. Findings The results of the study show that mechanical structures with a high degree of formalization and centralization have a negative impact on knowledge-based dynamic capability and integration has a positive relationship with dynamic capability. Moreover, the research shows that project team innovative performance is directly affected by knowledge generation and combination capability; however, knowledge acquisition/absorption does not interfere with project team innovative activity. Practical implications This study contributes to the managers of firms in the industrial sector by analyzing how the characteristics of organizational structure impact dynamic capability and project team innovative performance. The results of this study indicate that more mechanical structures have more difficulty in developing knowledge-based dynamic capability in the context of project teams. Originality/value This study advances the concept of knowledge-based dynamic capability from the firm level to the project team level. This study accesses a research gap that characterizes organizational structure as an antecedent of dynamic capability, analyzing the impact of organizational structure on the dimensions of dynamic capability and of the latter on project team innovative performance.

Knowledge Generation in Mid Size Entrepreneurial Firms

2005 ◽  
Vol 9 (4) ◽  
pp. 51-63 ◽  
Author(s):  
Anurag Mishra ◽  
M. Akbar
Keyword(s):  
Process Model ◽  
Case Analysis ◽  
Knowledge Generation ◽  
Generic Model ◽  
Entrepreneurial Firms ◽  
Generation Process ◽  
Knowledge Assets ◽  
Developed Markets ◽  
Knowledge Based ◽  
Line Diagram

Literature on medium sized enterprises (MSEs) is limited both in developed markets and emerging markets. This paper addresses this gap and explores MSEs from a knowledge-based perspective. Grounded in the case based research often MSEs, the paper identifies the knowledge assets employed by highly successful firms. The paper performs a detailed case analysis of three such firms from our sample. We trace the knowledge generation process through a detailed line diagram and based on the case analysis, build a generic model for analyzing the knowledge conversion process in MSEs. The contribution of this work is articulated in the process model that integrates the various classes of knowledge assets in the context of transitional firms in India. The paper also develops a few empirically testable propositions, filling a major gap in existing literature on knowledge management.

A logic of knowledge based on abstract arguments

2021 ◽  
Author(s):  
Yì N Wáng ◽  
Xu Li
Keyword(s):  
Computational Complexity ◽  
Model Checking ◽  
True Belief ◽  
Logical Omniscience ◽  
Knowledge Based ◽  
Logic Of Knowledge ◽  
Multi Agent ◽  
Soundness And Completeness ◽  
Base Logic

Abstract We introduce a logic of knowledge in a framework in which knowledge is treated as a kind of belief. The framework is based on a standard KD45 characterization of belief, and the characterization of knowledge undergoes the classical tripartite analysis that knowledge is justified true belief, which has a natural link to the studies of logics of evidence and justification. The interpretation of knowledge avoids the unwanted properties of logical omniscience, independent of the choice of the base logic of belief. We axiomatize the logic, prove its soundness and completeness and study the computational complexity results of the model checking and satisfiability problems. We extend the logic to a multi-agent setting and introduce a variant in which belief is characterized in a weaker system to avoid the problem of logical omniscience.

Multi-Granular Computing through Rough Sets

2014 ◽  
pp. 1-34 ◽  
Author(s):  
B. K. Tripathy
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Sets ◽  
Intelligent Systems ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Point Of View ◽  
Information Granules ◽  
Computing Platform

Granular Computing has emerged as a framework in which information granules are represented and manipulated by intelligent systems. Granular Computing forms a unified conceptual and computing platform. Rough set theory put forth by Pawlak is based upon single equivalence relation taken at a time. Therefore, from a granular computing point of view, it is single granular computing. In 2006, Qiang et al. introduced a multi-granular computing using rough set, which was called optimistic multigranular rough sets after the introduction of another type of multigranular computing using rough sets called pessimistic multigranular rough sets being introduced by them in 2010. Since then, several properties of multigranulations have been studied. In addition, these basic notions on multigranular rough sets have been introduced. Some of these, called the Neighborhood-Based Multigranular Rough Sets (NMGRS) and the Covering-Based Multigranular Rough Sets (CBMGRS), have been added recently. In this chapter, the authors discuss all these topics on multigranular computing and suggest some problems for further study.

Moving to an Online Framework for Knowledge-Driven Healthcare

2005 ◽  
pp. 136-149 ◽  
Author(s):  
Bruce Shadbolt ◽  
Rui Wang ◽  
Paul S. Craft
Keyword(s):  
Clinical Practice ◽  
Hypothesis Testing ◽  
Randomised Clinical Trial ◽  
Knowledge Generation ◽  
Service Research ◽  
Healthcare Organizations ◽  
Science Data ◽  
Clinical Trial Research ◽  
Knowledge Based ◽  
Clinical Investigations

The acquisition of knowledge in healthcare is mostly piecemeal and irregular. Consequently, we believe that the integration of science and patient care into a seamless framework is the key to establishing widespread knowledge-based healthcare organizations. Over the last five years, we have developed a dynamic methodology that completes the full information cycle using a generic online framework that merges science with clinical practice over the continuum of care. Called Protocol Hypothesis Testing (PHT), the framework is an extremely flexible web-enabled system that provides authors (expert groups) with the ability to instantly modify the structure of the system to meet the changing needs of clinical practice and incremental knowledge generation. The fully relational, centralised approach caters to the diversity of local needs whilst providing a global focus. The PHT System: • helps drive collaboration between clinicians, researchers, patients, and healthcare organizations to continually improve and use the latest and best evidence; • interfaces between clinical practice and bio-technology research;conducts randomised clinical trial research; centrally runs local clinical investigations and health service research;provides clinicians and patients with user-generated, decision-support algorithms and evidence-based summaries that are applicable to specific patients and their treatment choices; manages individual patient’s information, automatically distributing information to where it is needed, and providing patients with probable paths their treatment may follow; and provides a process to explore improvements in cost-effectiveness.In sum, the PHT system creates a centralised, seamless framework between research and clinical practice that is responsive to instant change based on hypothesis testing (science), data mining (exploration & thresholds) and expert opinion (authors) — all in the context of the needs of different diseases, clinical specialties and healthcare organisations.

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