Distributed Artificial Intelligence
The main objective of the Distributed Artificial Intelligence (DAI) specialization-track is to specialize students in the field of Artificial Intelligence (AI) by including all current approaches, namely:
Symbolic Artificial Intelligence: focused on the representation of knowledge and the modeling of the reasoning of intelligent systems (software or robots) called intelligent agents
Statistical Artificial Intelligence: focused on machine learning for the extraction of knowledge from massive data
Distributed Artificial Intelligence (DAI): using (and adapting when necessary) symbolic and statistical AI techniques, it focuses on the distribution of intelligence between different autonomous systems (called multi-agent systems) and the different types of interaction between these systems to achieve common goals collaboratively or individual goals in a competitive environment.
The DAI program provides students with a solid theoretical background. Following this program, students will have the opportunity to continue their studies in a doctoral thesis either in a public laboratory or in a research and development (R&D) department of private companies.
They will also have the opportunity to work as research engineers in R&D departments by applying the techniques taught in different areas of real applications, such as banking and insurance, medical diagnosis, risk management and mitigation, crisis management, anomaly detection, recommendation systems, self-driving cars, smart cities, compliance management, decision support systems and automated decision-making, internet of things, marketing, finance, legal reasoning, robotics, etc. Many large companies (Orange, RATP, SNCF, Airbus, Alstom, Safran, Thales, etc.) and startups are interested in the skills of our students by offering internships which in most cases lead to job offers.
Second Year (M2) Courses and Teachers
- Decision Theory : Elise Bonzon (Université Paris Cité)
- Agent-Oriented Learning : Bruno Bouzy (Université Paris Cité)
- Agent-Oriented Software Engineering : Nikos Spanoudakis (Technical University of Crete, Greece)
- Coalition formation and ressource allocation : Anaelle WILCZYNSKI (Ecole Centrale)
- Large Language Models (LLM) : Benoit Crabbé (UPCité)
- Automated Negotiation : Elise Bonzon (Université Paris Cité), Carles Sierra (IIIA, Spain)
- Computational Argumentation : Pavlos Moraitis (Université Paris Cité)
- Multi Agent Planning : Aurélie Beynier (Sorbonne U.)
- Automated Planning : Pavlos Moraitis (Université Paris Cité)
- Constraint Satisfaction : Christian Bessiere (CNRS – LIRMM, U. of Montpellier 2)