Michele Sevegnani

About

Michele Sevegnani is a leading researcher at the intersection of formal verification, cyber-physical systems (CPSs), and human-swarm interaction. His work focuses on ensuring the reliability and safety of autonomous systems, particularly within the context of Industry 4.0 and robot swarms. Sevegnani’s major contributions include developing adaptive model verification techniques for modularized Industry 4.0 applications, a critical step for integrating AI and sensor-driven decision-making in complex CPSs. He has also pioneered predictive formal modelling (PFM) at runtime to enhance operator situational awareness in human-swarm missions, addressing the challenge of maintaining human oversight in fault-tolerant, distributed robotic systems. His most cited work, "Adaptive Model Verification for Modularized Industry 4.0 Applications" (2022, 8 citations), lays foundational methods for runtime verification in dynamic industrial environments. Additionally, his user study on PFM (2025) provides empirical evidence for the effectiveness of formal methods in real-time human-swarm interaction. Through tools like CAN-verify for BDI agents, Sevegnani continues to bridge the gap between formal verification theory and practical, safe autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Adaptive Model Verification for Modularized Industry 4.0 Applications

   8 citations · 2022
2. 2
   Successful Swarms: Operator Situational Awareness with Modelling and Verification at Runtime

   4 citations · 2023
3. 3
   CAN-verify: A Verification Tool For BDI Agents

   3 citations · 2023
4. 4
   A User Study Evaluation of Predictive Formal Modelling at Runtime in Human-Swarm Interaction

   2 citations · 2025

Key Collaborators

Contact & Links