Air Lab

Artificial Intelligence & Robotics Laboratory



The Artificial Intelligence&Robotics Laboratory (AIR Lab) aims at developing innovative solutions for societal improvement, modernization, and progress.


A multi-disciplinary team, which includes computer scientists, engineers, psychologists, and lawyers, conducts both theoretical and experimental research related to Reinforcement Learning, Learning by Demonstration, and Computational Cognition.

The focus of our research is on Reinforcement Learning and the main research topics are safe reinforcement learning, multi-agent reinforcement learning, hierarchical reinforcement learning, learning by demonstrations and imitation learning, transfer learning, cognitive architectures, explainable AI, ethical AI, and AI legal framework compliance.

Application fields include Healthcare&Well Being, Business&Finance, Resource Management, Manufacturing, Robotics, and Telecommunications.


The Laboratory is organized into the following Research Units:

    • AI Theories, methods, and algorithms
    • AI Legal framework, Explainable AI, and Ethical AI
    • AI4Healthcare&Well Being
    • AI4Manufacturing, Resource Management, and Business&Finance
    • AI4Robotics and Telecommunications


National Projects
– Reshaping the Role of Measurement in the 4.0 Era: towards a Cyber-Physical Measurement System for Advanced Monitoring Applications, funded by the Italian Ministry of University and Research (MUR) – Bando PRIN 2022
– Innovative mathematical modelling for cell mechanics: global approach from micro-scale models to experimental validation integrated by Reinforcement Learning, funded by the Italian Ministry of University and Research (MUR) – Bando PRIN PNRR 2022
Regional Projects
– EVOOLIO – l’EVOluzione dell’OLIO evo del sannio tracciato con la blockchain, funded by Campania Region – Misura 16 “Cooperazione”, Azione 2 – 2022



  • Pia Addabbo
  • Antonio Coronato
  • Matteo Cutugno
  • Raffaele De Luca Picione
  • Ida D’Ambrosio
  • Katia La Regina
  • Muddasar Naeem
  • Fabrizio Stasolla
  • Giancarlo Tretola
  • Zaib Ullah

PHD Students

  • Marco Barone
  • Matteo Ciaschi

Associate Members

  • Mussarat Abbas, Quaid-i-Azam University Islamabad, Pakistan

List of Publications

  1. An AI-empowered infrastructure for risk prevention during medical
    examination; SIH Shah, Muddasar Naeem, G Paragliola, A Coronato, M Pechenizkiy;
    Expert Systems with Applications 225, 120048
  2. Blockchain applications in sustainable smart cities; Zaib Ullah, Muddasar Naeem,
    Antonio Coronato, Patrizia Ribino, Giuseppe De Pietro, Sustainable Cities and Society
  3. Preface to the Proceedings of the 3rd International Workshop on
    Self-Learning in Intelligent Environments (SeLIE’23); P Ribino, A Coronato, M
    Esposito, M Naeem; Workshop Proceedings of the 19th International Conference on
    Intelligent Environments
  4. Optimal user scheduling in multi antenna system using multi agent
    reinforcement learning; Muddasar Naeem, A Coronato, Z Ullah, S Bashir, G Paragliola;
    Sensors 22 (21), 8278
  5. Learning and assessing optimal dynamic treatment regimes through
    cooperative imitation learning; SIH Shah, A Coronato, M Naeem, G De Pietro; EEE
    Access 10, 78148-78158
  6. Projection based inverse reinforcement learning for the analysis of dynamic
    treatment regimes; SIH Shah, G De Pietro, G Paragliola, A Coronato; Applied Intelligence
    53 (11), 14072-14084
  7. Self-Adapted Resource Allocation in V2X Communication; M Jamal, Z Ullah, M
    Abbas; Workshop Proceedings of the 19th International Conference on Intelligent
  8. Cloud-Based Monitoring System for Personalized Home Medication; A Ismail, M
    Fiorino, M Abbas, MH Syed, Z Ullah; Workshop Proceedings of the 19th International
    Conference on Intelligent
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