The research activity lies within the fields of Artificial Intelligence and Optimization, with particular emphasis on bio-inspired models, Swarm Intelligence, and multi-agent systems. Within this framework, computational models inspired by natural collective behaviors are studied and developed, with specific focus on Ant Colony Optimization (ACO) and its agent-based extensions applied to navigation and path-finding problems in static and dynamic environments.
In parallel, Agent-Based Modeling is employed to analyze collective dynamics, decision-making processes, and trust mechanisms among agents and between agents and their environment, interpreting algorithmic parameters also from a behavioral and social perspective. A significant part of the research is further devoted to autonomous navigation in unknown or partially known environments, where agents integrate memory, local perception, energy constraints, and adaptive strategies in order to balance exploration and efficiency.
The activity includes the development of computational models, implementation in NetLogo and/or Python, the design of controlled experimental campaigns, statistical analysis of results, and comparative validation of cooperative, competitive, and imitative strategies in complex distributed systems.
