scholarly journals Final LDRD report human interaction with complex systems: advances in hybrid reachability and control.

2006 ◽  
Author(s):  
Meeko M Oishi
Keyword(s):  
Complex Systems ◽  
Human Interaction ◽  
And Control

Introduction

2018 ◽  
Author(s):  
Xiaojun Bi ◽  
Andrew Howes ◽  
Per Ola Kristensson ◽  
Antti Oulasvirta ◽  
John Williamson
Keyword(s):  
Information Visualization ◽  
User Interfaces ◽  
Graphic Design ◽  
Interaction Design ◽  
Human Interaction ◽  
Learning Design ◽  
Human Factors Engineering ◽  
Interaction Techniques ◽  
Iterative Design ◽  
And Control

This chapter introduces the field of computational interaction, and explains its long tradition of research on human interaction with technology that applies to human factors engineering, cognitive modelling, artificial intelligence and machine learning, design optimization, formal methods, and control theory. It discusses how the book as a whole is part of an argument that, embedded in an iterative design process, computational interaction design has the potential to complement human strengths and provide a means to generate inspiring and elegant designs without refuting the part played by the complicated, and uncertain behaviour of humans. The chapters in this book manifest intellectual progress in the study of computational principles of interaction, demonstrated in diverse and challenging applications areas such as input methods, interaction techniques, graphical user interfaces, information retrieval, information visualization, and graphic design.

Integrated decision and control of human-engineered complex systems

2006 ◽  
Vol 35 (3) ◽  
pp. 275-294 ◽  
Author(s):  
D. K. Tolani ◽  
A. Ray ◽  
J. F. Horn
Keyword(s):  
Complex Systems ◽  
And Control

Industry 4.0 From the Systems Engineering Perspective

2020 ◽  
pp. 575-599
Author(s):  
Vladimír Bureš
Keyword(s):  
Complex System ◽  
Physical Activity ◽  
Complex Systems ◽  
Frequency Analysis ◽  
Systems Engineering ◽  
Industry 4.0 ◽  
Control Level ◽  
Design Development ◽  
Starting Point ◽  
And Control

Systems engineering focuses on design, development, and implementation of complex systems. Not only does the Industry 4.0 concept consist of various technical components that need to be properly set and interconnected, but it is also tied to various managerial aspects. Thus, systems engineering approach can be used for its successful deployment. Overemphasis of technological aspects of Industry 4.0 represents the main starting point of this chapter. Then, collocation analysis, word clusters identification, selection and exemplification of selected domain in the business management realm, and frequency analysis are used in order to develop a holistic framework of Industry 4.0. This framework comprises six levels – physical, activity, outcome, content, triggers, and context. Moreover, the information and control level is integrated. The new holistic framework helps to consider Industry 4.0 from the complex systems engineering perspective – design and deployment of a complex system with required parameters and functionality.

scholarly journals Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

2020 ◽  
Vol 34 (10) ◽  
pp. 13730-13731
Author(s):  
Ece C. Mutlu
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Complex Systems ◽  
Bayesian Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Probabilistic Models ◽  
Human Interaction ◽  
Multi Agent Systems ◽  
Diffusion Dynamics

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

scholarly journals Corrigendum to “Advanced Topics in Modeling, Bifurcation Analysis, and Control Theory of Complex Systems”

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-1
Author(s):  
Olfa Boubaker ◽  
Sajad Jafari ◽  
Christos Volos ◽  
Zeraoulia Elhadj ◽  
Viet-Thanh Pham ◽  
...  
Keyword(s):  
Control Theory ◽  
Complex Systems ◽  
Bifurcation Analysis ◽  
And Control
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