Papers
6
Total Citations
276
H-Index
4
About
Antonio Coronato is a leading researcher at the intersection of artificial intelligence, reliable software engineering, and intelligent environments. His work primarily focuses on reinforcement learning (RL) and its applications, particularly in healthcare and ambient assisted living. Coronato has made significant contributions to advancing RL methodologies, including a highly influential 2020 paper on reinforcement learning applications that has garnered 241 citations, establishing it as a foundational reference in the field. He has pioneered the use of inverse reinforcement learning to enable intelligent environments to learn complex human tasks by observing user behavior, a breakthrough for adaptive systems. Beyond AI, Coronato has shaped the discipline of high-quality medical software engineering, addressing regulations, standards, and certification for safety-critical healthcare systems. His work on formal modeling of socio-technical collective adaptive systems has provided frameworks for understanding complex, unpredictable interactions between humans and technology. As the founding Editor-in-Chief of the Journal of Reliable Intelligent Environments, Coronato has helped define the field's standards. His research continues to bridge theoretical advances in machine learning with practical, reliable implementations in healthcare and smart environments, making him a pivotal figure in creating trustworthy, adaptive intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Formal Modeling of Socio-technical Collective Adaptive Systems13 citations · 2012
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- 6Inverse Reinforcement Learning Through Max-Margin Algorithm3 citations · 2021