Novel Fuzzy Systems for Human-Autonomous Agent Teaming

Chang, Yu-Cheng

Novel Fuzzy Systems for Human-Autonomous Agent Teaming

Chang, Yu-Cheng

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Publication Type:: Thesis
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Chang, Yu-Cheng
dc.date.accessioned	2022-06-22T03:25:55Z
dc.date.available	2022-06-22T03:25:55Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/10453/158311
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The Multi-agent Teaming (MAT) systems that have been widely applied in many fields provide a novel method for establishing models, conducting the analysis, implementing complex tasks and so on. The agents in an MAT system can be defined as intelligent agents, machine agents and human agents based on a particular task to exhibit flexible behaviours. This research investigates various fuzzy models to resolve the problems of designing MAT systems. The main contributions are as follows. 1) For multiple-agent coordination, a hierarchical fuzzy system is proposed and applied to navigation and simultaneous arrival of mobile agents. 2) Explainable fuzzy systems are proposed. We developed an interpretable fuzzy model for human agents to understand the decision rules learned by machine agents and a fuzzy rule information visualisation framework for machine agents to understand human cognitive states. 3) Finally, the distributed fuzzy system is proposed to resolve the data privacy and high-dimensional data in designing MAT systems. A novel consensus learning is developed for the distributed fuzzy system to learn antecedent and consequent components.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/158311/2/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Novel Fuzzy Systems for Human-Autonomous Agent Teaming	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The Multi-agent Teaming (MAT) systems that have been widely applied in many fields provide a novel method for establishing models, conducting the analysis, implementing complex tasks and so on. The agents in an MAT system can be defined as intelligent agents, machine agents and human agents based on a particular task to exhibit flexible behaviours. This research investigates various fuzzy models to resolve the problems of designing MAT systems. The main contributions are as follows. 1) For multiple-agent coordination, a hierarchical fuzzy system is proposed and applied to navigation and simultaneous arrival of mobile agents. 2) Explainable fuzzy systems are proposed. We developed an interpretable fuzzy model for human agents to understand the decision rules learned by machine agents and a fuzzy rule information visualisation framework for machine agents to understand human cognitive states. 3) Finally, the distributed fuzzy system is proposed to resolve the data privacy and high-dimensional data in designing MAT systems. A novel consensus learning is developed for the distributed fuzzy system to learn antecedent and consequent components.

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http://hdl.handle.net/10453/158311