Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie:
- 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)
I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati.
- 9-12 crediti ECTS in basi teoriche ampliate (FTP)
I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione
- 6-9 crediti ECTS in moduli di contesto (CM)
I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.
La descrizione del modulo (scarica il pdf)riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:
Natural, social, and engineered complex systems can be modelled as being composed of agents interacting with one another and their environment. This course introduces students to the theory, tools and techniques for understanding and solving problems related to such systems.
The course is composed of two parts. In the first one, both cooperative and selfish agents and interactions between them will be discussed. The methodological support will be provided by game theory.
In the second part, the focus will be on the study and analysis of models of systems in the aim of understanding the conditions under which certain properties can emerge and agent might learn certain strategies or behaviours by interacting with the environment and themselves.
Throughout the course, several application areas such as cooperation and competition, social influence and reinforcement learning will be discussed.
Basic knowledge of probability, algebra, calculus and differential equations. Basics of procedural programming and ability to implement small programs in an arbitrary language, e.g. Python, Matlab, R, Java, C#, C++, C, etc.
Obiettivi di apprendimento
A successful participant of this course is able to
- understand the rationale of multi-agent systems and their modelling.
- model scenarios with multiple interacting agents in the language of game theory
- evaluate the feasibility of achieving goals with agents using game theory
- understand the basic approaches to multi-agent learning, their peculiarities and their differences
- learn to choose the appropriate class of models with agents to characterise different complex systems
- implement in an efficient way a model of a system, then understand and analyse the corresponding outputs
- Multi-agent interaction: games in normal form, dominant strategies, Nash equilibria, Pareto optimality, partial observability, cooperative and coalition games, Shapley value, repeated games,
- Multi-agent learning: model based (fictitious and rational learning) and model-free (no regret and reinforcement learning) approaches
- Population dynamics: evolutionarily stable strategies, Replicator’s dynamics
Metodologie di insegnamento e apprendimento
- Exercises and homework
- Practical work with appropriate tools
- Literature studies
- A Concise Introduction to Multi-Agent Systems and Distributed Artificial Intelligence. Nikos Vlassis. Morgan & Claypool Publishers, 2007.
- Introduction to Multi-Agent Systems - 2nd Edition. Michael Wooldridge. John Wiley & Sons, 2009.
- Multi-Agent Systems. Yoav Shoham and Kevin Leyton-Brown. Cambridge University Press, 2009.
- Artificial Intelligence, A Modern Approach (4th Edition). Stuart Russell and Peter Norvig. Pearson. 2021