
Dr Xin Yuan
ARC Senior Research Fellow
School of Electrical and Mechanical Engineering
Faculty of Sciences, Engineering and Technology
Xin Yuan, a.k.a. Vernon, is fascinated by the effects of Artificial Intelligence (AI) and autonomous machines on our contemporary daily lives and the innovations they brought to the industry. He believes that related research should not be limited to “narrow” domains. As a result, he is now enthusiastically involved in many developments of intelligent robotic systems to investigate more efficient approaches to Human-like intellectual functions. He focuses on discovering novel methods of constructing Artificial General Intelligence and Advanced Uncrewed systems.
Currently, Vernon is undertaking research at the University of Adelaide and working on discovering new approaches for the development of cognitive agent-based systems with AI and their impacts on the manufacturing, transportation and healthcare sectors. He teaches courses related to digital, electronics and autonomous systems. He has also supervised and led a long list of projects for developing advanced AI-enabled autonomous systems and applications.
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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Appointments
Date Position Institution name 2021 - ongoing Senior Research Fellow University of Adelaide 2017 - 2021 P/T Lecturer University of Adelaide -
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 2016 - 2021 University of Adelaide Australia PhD 2012 - 2015 University of Adelaide Australia Honour Bachelor Degree of Engineering -
Research Interests
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Journals
Year Citation 2022 Yuan, X., Liebelt, M. J., Shi, P., & Phillips, B. J. (2022). Cognitive decisions based on a rule-based fuzzy system. Information Sciences, 600, 323-341.
Scopus2 WoS12022 Fei, Y., Shi, P., Lim, C. P., & Yuan, X. (2022). Finite-Time Observer-Based Formation Tracking With Application to Omni-Directional Robots. IEEE Transactions on Industrial Electronics, 1-9.
2022 Lian, Z., Shi, P., Lim, C. C., & Yuan, X. (2022). Fuzzy-model-based lateral control for networked autonomous vehicle systems under hybrid cyber-attacks. IEEE Transactions on Cybernetics, PP, 1-10.
Scopus6 WoS152021 Yu, J., Li, Q., Xing, W., Yuan, X., & Shi, Y. (2021). Event-Based Consensus Tracking for Nonlinear Multi-Agent Systems Under Semi-Markov Jump Topology. IEEE ACCESS, 9, 135868-135878.
2021 Yuan, X., Liebelt, M. J., Shi, P., & Phillips, B. J. (2021). Creating rule-based agents for artificial general intelligence using association rules mining. International Journal of Machine Learning and Cybernetics, 12(1), 223-230.
Scopus2 WoS4 -
Conference Papers
Year Citation 2022 Fei, Y., Yuan, X., Shi, P., & Lim, C. (2022). Neural-Based Hierarchical Formation Control of Mixed-Order Multi-Agent Systems. In 2021 International Conference on Machine Learning and Cybernetics (ICMLC) Vol. 2021-December (pp. 1-6). online: IEEE.
WoS22020 Fei, Y., Yuan, X., Shi, P., & Lim, C. C. (2020). Uncertainty-Observer-Based Dynamic Sliding Mode Formation Control of Multi-agent Systems. In 2020 IEEE International Conference on Machine Learning and Cybernetics (ICMLC) Vol. 2020-December (pp. 82-87). online: IEEE.
2019 Yuan, X., Liebelt, M. J., Shi, P., & Phillips, B. J. (2019). Development of rule-based agents for autonomous parking systems by association rules mining. In Proceedings of the 19th International Conference on Machine Learning and Cybernetics (ICMLC 2019) Vol. 2019-July (pp. 1-6). Piscataway, NJ: IEEE.
WoS12017 Yuan, X., Liebelt, M., & Phillips, B. (2017). A cognitive approach for reproducing the homing behaviour of honey bees. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence Vol. 2 (pp. 543-550). Portugal: SCITEPRESS.
Scopus3 WoS3 -
Software
Year Citation - Yuan, X. (n.d.). Multi-Approaches to Achieve an Advanced Cognitive Agent in a New Type of Parallel Processing Computer [Computer Software].
Date | Project Title | Investigators | Funding Body | Amount |
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2021 | Remote Operations - Shadow Robots - Stage 1 | Shi P, Yuan X | Australian Meat Processor Corporation & Meat and Livestock Australia | AUD 165,000 |
2021 | Collaborative Robots Evaluation and Deployment Strategy Development – Stage 2 | Shi P, Yuan X | Australian Meat Processor Corporation & Meat and Livestock Australia | AUD 34,000 |
2021 | AGVs (with optional integrated collaborative robots) - Stage 2 | Shi P, Yuan X | Australian Meat Processor Corporation & Meat and Livestock Australia | AUD 34,000 |
2021 | A systematic solution for Innovation Challenges of Australian Meat Processor Corporation | Shi P, Yuan X | Australian Meat Processor Corporation & Meat and Livestock Australia | AUD 9,857.10 |
2021 | Bone Belt Monitoring - Stage 2 | Shi P, Wu Q, Yuan X | Australian Meat Processor Corporation & Meat and Livestock Australia | AUD 55,000 |
2021 | Beef Butt (HQ) Deboning Automation – Stage 2 | Shi P, Wu Q, Yuan X | Australian Meat Processor Corporation & Meat and Livestock Australia | AUD 235,000 |
Date | Taught | Developed | Coordinated | Course Title | Institution | Course Level/ Code |
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2017 | x | Analog Electronics | The University of Adelaide | ELEC ENG 1100 | ||
2017 | x | Electronic Systems | The University of Adelaide | ELEC ENG 1101 | ||
2017 | x | Digital Electronics | The University of Adelaide | ELEC ENG 1102 | ||
2018 | x | Analog Electronics | The University of Adelaide | ELEC ENG 1100 | ||
2018 | x | Electronic Systems | The University of Adelaide | ELEC ENG 1101 | ||
2018 | x | Digital Systems | The University of Adelaide | ELEC ENG 2100 | ||
2018 | x | Digital Electronics | The University of Adelaide | ELEC ENG 1102 | ||
2018 | x | x | x | Autonomous Systems | The University of Adelaide | ELEC ENG 4107 |
2019 | x | Analog Electronics | The University of Adelaide | ELEC ENG 1100 | ||
2019 | x | x | Electronic Systems | The University of Adelaide | ELEC ENG 1101 | |
2019 | x | Digital Systems | The University of Adelaide | ELEC ENG 2100 | ||
2019 | x | x | Digital Electronics | The University of Adelaide | ELEC ENG 1102 | |
2019 | x | x | x | Autonomous Systems | The University of Adelaide | ELEC ENG 4107 |
2020 | x | Analog Electronics | The University of Adelaide | ELEC ENG 1100 | ||
2020 | x | Electronic Systems | The University of Adelaide | ELEC ENG 1101 | ||
2020 | x | Digital Systems | The University of Adelaide | ELEC ENG 2100 | ||
2020 | x | x | Digital Electronics | The University of Adelaide | ELEC ENG 1102 | |
2021 | x | x | Analog Electronics | The University of Adelaide | ELEC ENG 1100 | |
2021 | x | x | Electronic Systems | The University of Adelaide | ELEC ENG 1101 | |
2021 | x | Digital Systems | The University of Adelaide | ELEC ENG 2100 | ||
2021 | x | Digital Electronics | The University of Adelaide | ELEC ENG 1102 | ||
2021 | x | Research Methods and Project Management | The University of Adelaide | ENG 3005 |
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