Simulation and Modelling based on
AI, ML and Intelligent Agents techniques
In today’s fast-evolving scientific world, the connection between modeling, simulation, and artificial intelligence (AI) is becoming stronger and more impactful. AI and Machine Learning (ML) are now widely used to make simulations faster and more efficient, allowing complex processes to be streamlined. At the same time, simulations provide valuable data that helps AI and ML systems learn and improve, making them more effective across a range of industries.
One key area of progress is the use of intelligent agents—AI-powered systems that can simulate complex environments and make decisions. These agents are proving highly successful in areas such as smart cities, healthcare, autonomous vehicles, and resource management, where they can model intricate systems with great accuracy. By using AI, industries can predict outcomes, optimize processes, and adapt to changes in real-time, offering powerful tools for tackling challenges in fields as diverse as robotics, cybersecurity, and urban planning.
This growing synergy between AI and simulations opens up new opportunities for innovation, allowing businesses and researchers to solve problems that were once too complex to manage.
This track delves into the relationship between AI, ML, intelligent agent techniques, and modeling & simulation, exploring their potential from multiple perspectives:
- Simulation models of autonomous agents driven by AI and ML.
- Simulations of multi-agent systems with complex dynamics and AI-based decision-making.
- Validation of AI systems in controlled or simulated environments.
- Reinforcement learning applications.
- AI and machine learning techniques for modeling and simulation in real-world applications (e.g., industry, smart cities, robotics, cybersecurity, resource management, water and energy systems, healthcare, urban mobility).
Track Chairs:
- Prof. Salvatore Cavalieri (University of Catania), e-mail: salvatore.cavalieri@unict.it
- Prof.Concetto Spampinato (University of Catania), e-mail: concetto.spampinato@unict.it
Track co-Chairs:
- Prof. Simone Palazzo (University of Catania), e-mail: simone.palazzo@unict.it
- Dott.Sarah Di Grande (University of Catania), e-mail: sarah.digrande@phd.unict.it
- Dott.Mariaelena Berlotti (University of Catania), e-mail: mariaelena.berlotti@phd.unict.it
- Dott. Thamires De Souza Oliveira (Unversity of Catania), e-mail: t.desouzaoliveira@unict.it