I have been working as a Researcher at the Institute of Cognitive Sciences and Technologies since 2014, and I work as a permanent Researcher since 2022. In 2019, I got the PhD in Computing at the faculty of Electronics and Mathematics of the Plymouth University (United Kingdom).
My research interests cover different topics. The main focus is on two areas: the study of evolutionary algorithms used to synthesize neural network controllers for autonomous robots (Evolutionary Robotics) and techniques for the effective evolution of neural networks (Neuroevolution). Moreover, I investigate the relationship between evolution and learning in autonomous agents and the co-evolution of body and brain in agents. I am also interested in the analysis of collective decision making and collaborative behaviors in groups of robots (Swarm Robotics), the emergence of cooperation and competition in agents as well as the co-evolution of competitive behaviors in autonomous agents. Another relevant research field concerns the application of Deep Learning both in industrial contexts (Image classification) and for the assessment of behavioral quality in the elderly. Finally, other research interests regard assistive robotics, educational robotics, bioinformatics and genomics.
I took part in the SI Robotics project from 2020 to 2022. Currently, I participate as substitute Principal Investigator (PI) in the PRIN2022 "Insights into the fast genome evolution of Gibbons through single-cell strand sequencing and simulation-based approaches" project.
List of publications:
Paolo Pagliuca and Davide Yuri Inglese (2025). The Importance of Functionality over Complexity: A Preliminary Study on Feed-Forward Neural Networks. In Advanced Neural Artificial Intelligence: Theories and Applications (pp. 447-458). Singapore: Springer Nature Singapore.
Paolo Pagliuca and Alessandra Vitanza (2025). A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis. IEEE Access, vol. 13, pp. 72721-72735.
Stefano Nolfi and Paolo Pagliuca (2025). Global Progress in Competitive Co-Evolution: a Systematic Comparison of Alternative Methods. Frontiers in Robotics and AI, vol. 11.
Paolo Pagliuca (2024). Analysis of the Exploration-Exploitation Dilemma in Neutral Problems with Evolutionary Algorithms. Journal of Artificial Intelligence and Autonomous Intelligence, vol. 1, issue 2, pp. 110-121.
Alessandra Vitanza, Federica Morleo and Paolo Pagliuca (2024). Can Educational Robotics Experiences Shape Early Childhood Interpersonal Dynamics? An Exploratory Investigation. 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 873-878.
Alessandra Vitanza, Federica Morleo and Paolo Pagliuca (2024). Interazione Uomo-Robot: l’importanza degli aspetti psicologici nella cura degli anziani. Sistemi intelligenti, Rivista quadrimestrale di scienze cognitive e di intelligenza artificiale, 3/2024, pp. 631-640.
Paolo Pagliuca (2024). Learning and Evolution: Factors Influencing an Effective Combination. AI, vol. 5, issue 4, pp. 2393-2432.
Giulia Tufo, Meriam Zribi, Paolo Pagliuca and Francesca Pitolli (2024). An Explainable Convolutional Neural Network for the Detection of Drug Abuse. Proceedings of the First Workshop on Explainable Artificial Intelligence for the medical domain (EXPLIMED 2024), vol. 3831.
Meriam Zribi, Paolo Pagliuca and Francesca Pitolli (2024). Enhancing Industrial Quality Control Efficiency: an Innovative Deep Learning Approach for Sustainable Process Monitoring. Proceedings of the 9th European Congress on Computational Methods in Applied Sciences (ECCOMAS 2024).
Meriam Zribi, Paolo Pagliuca and Francesca Pitolli (2024). A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories. Expert Systems with Applications, vol. 256, Elsevier.
Paolo Pagliuca and Alessandra Vitanza (2024). Enhancing Aggregation in Locomotor Multi-Agent Systems: a Theoretical Framework. 25th Workshop From Objects to Agents (WOA 2024), vol. 3735, pp. 42-57.
Paolo Pagliuca and Alessandra Vitanza (2024). The role of n in the n-mates evaluation method: a quantitative analysis. Proceedings of the 2024 Artificial Life Conference (ALIFE 2024), MIT Press, pp. 812-814.
Paolo Pagliuca and Alessandra Vitanza (2023). N-Mates Evaluation: a New Method to Improve the Performance of Genetic Algorithms in Heterogeneous Multi-Agent Systems. 24th Workshop From Objects to Agents (WOA 2023), vol. 3579, pp. 123-127.
Alessandra Vitanza, Paolo Pagliuca, Filippo Cantucci and Stefano Nolfi (2023). Skeleton Timed Up and Go on MARIO Robot. 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 1171-1176.
Paolo Pagliuca and Alessandra Vitanza (2023). Evolving aggregation behaviors in swarms from an evolutionary algorithms point of view. In Applications of Artificial Intelligence and Neural Systems to Data Science (pp. 317-328). Singapore: Springer Nature Singapore.
Paolo Pagliuca, Davide Yuri Inglese and Alessandra Vitanza (2023). Measuring emergent behaviors in a mixed competitive-cooperative environment. International Journal of Computer Information Systems and Industrial Management Applications, vol. 15, pp. 69-86.
Paolo Pagliuca and Alessandra Vitanza (2022). Self-organized Aggregation in Group of Robots with OpenAI-ES. International Conference on Soft Computing and Pattern Recognition, pp. 770-780.
Paolo Pagliuca, Nicola Milano and Stefano Nolfi (2022). Automated Categorization of Behavioral Quality Through Deep Neural Networks. 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 372-376.
Paolo Pagliuca and Stefano Nolfi (2022). The dynamic of body and brain co-evolution. Adaptive Behavior, vol. 30 (3), pp. 245-255.
Paolo Pagliuca, Nicola Milano and Stefano Nolfi (2020). Efficacy of modern neuro-evolutionary strategies for continuous control optimization. Frontiers in Robotics and AI, vol. 7 (98).
Paolo Pagliuca and Stefano Nolfi (2019). Robust optimization through neuroevolution. PLOS ONE, vol. 14 (3).
Nicola Milano, Paolo Pagliuca and Stefano Nolfi (2019). Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits. Evolutionary Intelligence, vol. 12, pp. 83-95.
Paolo Pagliuca, Nicola Milano and Stefano Nolfi (2018). Maximizing adaptive power in neuroevolution. PLOS ONE, vol. 13 (7).
Paolo Pagliuca and Stefano Nolfi (2015). Integrating learning by experience and demonstration in autonomous robots. Adaptive Behavior, vol. 23 (5), pp. 300-314.
