Papers

3

Total Citations

21

H-Index

3

About

Hj. Mohd Asri Hj. Mansor is a researcher advancing the frontier of autonomous systems through lightweight, self-learning artificial intelligence. His work centers on enabling machines—particularly mobile robots—to adapt and react intelligently to unforeseen events without relying solely on preprogrammed responses. His most-cited paper (2016, 10 citations) introduces a hybrid AI algorithm that empowers autonomous systems to modify their behavior in real time, moving beyond static, expert-defined rules. In related work (2015, 7 citations), he demonstrates a novel simulation of mobile robot navigation that combines reinforcement learning with unsupervised weightless neural networks, allowing the system to discover optimal behaviors through trial and error. A third key contribution (2017, 4 citations) refines this approach by using weightless neural networks as autonomous state classifiers, reducing the need for human experts to predefine every possible state and action. Together, these papers form a cohesive body of work that pushes toward more flexible, efficient, and truly self-learning autonomous agents. Mansor’s research is particularly relevant for applications in robotics, automation, and intelligent systems where adaptability is critical.

Research Focus

Key Achievements

3
H-Index
3
Papers
21
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Formulation of a lightweight hybrid AI algorithm towards self-learning autonomous systems
10 citations · 2016
📈 Most Prolific Year: 2016 (1 Papers)
🤝 Key Collaborators: 3
🏛 Institutions: Universiti Teknologi MARA, Universiti Teknologi MARA System, University of Kuala Lumpur

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 16 days ago