Mr Xin Yuan
Higher Degree by Research Candidate
Postgraduate Candidate
School of Electrical and Electronic Engineering
Faculty of Engineering, Computer and Mathematical Sciences
Xin Yuan, also known as Vernon, is fascinated by the effects of AI on our contemporary daily lives. As a result, he is now enthusiastically involved in many developments of intelligent robotic systems to investigate more efficient approaches to AI functions and is focusing on methods of constructing Artificial General Intelligence, Autonomous Unmanned systems and Agent-based systems. He believes that, by analysing decision-making processes and behaviours of cognitive creatures, AI functions could be lifted and have more advanced impacts on our lives.
Currently, Xin Yuan is undertaking research at the University of Adelaide on a new form of cognitive architecture, which is using a parallel processing production rule-based structure. This research aims to investigate new approaches to develop cognitive agents with Artificial General Intelligence, which is to build machines with capabilities of understanding and learning intellectual tasks as a human does. There are two major challenges, which are how could we process data and information to be represented in a cognitive term, and what is the structure for making decisions as intelligent creatures. The investigation found that, in cognitive behaviours, information captured are fuzzy, and unnecessary details are ignored. Therefore, before decision-making, numerical values need to be converted into a fuzzy symbolic representation which describes the general situation in a “wordy” term. On the other hand, cognitive decision-making has a structure with IF-THEN rules at different levels. In this structure, fundamental information triggers to become knowledge and then knowledge provides an understanding of the situations. Eventually, a decision will be made while a serial of rules is triggered. In this research, an under-development computational architecture is used to process with only necessary environmental information and triggers a chain of knowledge-based rules to make a decision. The research contributes to demonstrating a new approach to build smarter machines in general, over “narrowed” AI functions.
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Language Competencies
Language Competency Chinese (Cantonese) Can read, write, understand spoken and peer review Chinese (Mandarin) Can read, write, speak, understand spoken and peer review English Can read, write, speak, understand spoken and peer review Japanese Can understand spoken -
Education
Date Institution name Country Title 2012 - 2015 University of Adelaide Australia Honour Bachelor Degree of Engineering -
Research Interests
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Conference Papers
Analog Electronics - Tutor and Practical Demonstrator (2017);
Electronic Systems - Tutor and Practical Demonstrator (2017);
Digital Electronics - Tutor and Practical Demonstrator (2017);
Analog Electronics - Tutor and Practical Demonstrator (2018);
Electronic Systems - Tutor and Practical Demonstrator (2018);
Digital Systems - Tutor and Practical Demonstrator (2018);
Digital Electronics - Tutor and Practical Demonstrator (2018);
Autonomous Systems - Practical Coordinator and Demonstrator (2018);
Analog Electronics - Tutor (2019);
Electronic Systems - Tutor (2019);
Digital Systems - Tutor (2019);
Autonomous Systems - Practical Coordinator and Demonstrator (2019);
Digital Electronics - Tutor and Practical Demonstrator (2019);
Analog Electronics - Tutor (2020);
Electronic Systems - Tutor (2020);
Digital Systems - Tutor (2020);
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