Distributed Agents, Robotics, Operations Research, Interaction, Decision Making (ANDROIDE)

The ANDROIDE course covers topics related to problem-solving, agents, decision-making and autonomous robotics. This training aims to provide both theoretical and practical education covering all the main areas of artificial intelligence, decision, operational research and interaction; in particular, it addresses all aspects related to "problem-solving" that economic professionals must face, as well as those related to the implementation of intelligent interaction processes, whether with a human user (for example, to acquire information relevant to problem- solving) or between autonomous entities, such as artificial agents or robots.

The current boom in artificial intelligence, decision support and robotics tools calls for the training of experts who can master a wide range of techniques, both symbolic and numerical, and who are able to propose innovative solutions in their professional environment, whether academic or industrial. The objective of the course is to train specialists in ICST (Information and Communication Sciences and Technologies), enabling them to master the concepts, models and tools (particularly algorithmic) of these themes.

Distributed Agents, Robotics, Operations Research, Interaction, Decision Making (ANDROIDE)


The pedagogical objective of the Androide course is to provide fundamental knowledge in the following areas

  • interactive environments: virtual environments, human-computer interaction, serious games, video games, e-learning, information systems
  • decision: decision theory, preference modeling and learning, multi-objective or multi-agent combinatorial optimization, Bayesian networks
  • Robotics and intelligent systems: agent and autonomous robot, multi-agent systems, machine learning
  • Operational research: mathematical programming, optimization and complexity, graphs and scheduling



This innovative teaching ensures the training of future specialists, both engineers and researchers, in a rapidly expanding field.

Opportunities in the business world:

  • High-tech companies: video games, e-learning, industrial and domestic robotics,
  • Major web players and software publishers,
  • Large industrial groups: transport, banking, energy, etc.
  • Consulting firms.

Opportunities in the world of research and teaching:

  • PhD in France or abroad,
  • Public, private or mixed research (CIFRE theses).



First year (M1 - S1)

5 UEs among the following UEs:

Acronym Title Person(s) in charge ECTS Course  
MOGPL Modeling, Optimization, Graphs, and Linear Programming Patrice Perny 6 ANDROIDE Mandatory
LRC Logic and Knowledge Representations Jean-Gabriel Ganascia, Nicolas Maudet 6 ANDROIDE/DAC Mandatory
MAPSI Probabilistic and Statistical Models and Algorithms for Computer Science   6 DAC/IMA Highly recommended
COMPLEX Complexity, Probabilistic and Approximate Algorithms   6 SFPN Highly recommended
IL Software Engineering   6 STL Recommended
AAGB Introduction to biology and algorithms on trees, and graphs in bioinformatics   6 BIM Recommended
MLBDA Advanced Database Models and Languages   6 DAC Recommended
BIMA Basics of Image Processing   6 IMA Recommended
MODEL     6 SFPN Recommended
PSCR Concurrent and Distributed System Programming   6 SAR Recommended
ALGAV Advanced Algorithms   6 STL Recommended
DLP Development of Programming Languages   6 STL Recommended

Second semester (M1 - S2)

5 UEs among the following UEs. P-ANDROID, RA and English are mandatory. You must then choose exactly 3 UEs among those marked "Complementary", including at least 2 UEs related to Androides that are "Complementary".

Acronym Title Person(s) in charge ECTS Course  
PANDROIDE ANDROIDE project Nicolas Bredeche, Nicolas Maudet 6 ANDROIDE Mandatory
English English   3 Dept. Langues Mandatory
RA Robotics and Learning Olivier Sigaud 3 ANDROIDE Mandatory
RP Problem Solving Evripidis Bampis 6 ANDROIDE Complementary
FoSyMa Foundations of Multiagents Systes Aurélie Beynier 6 ANDROIDE Complementary
IHM Human-Computer Interaction Gilles Bailly 6 ANDROIDE Complementary
DJ Decision and Games Pierre-Henri Wuillemin 6 ANDROIDE Complementary
IAMSI Artificial Intelligence and Symbolic Information Handling   6 DAC Complementary
ML Machine Learning   6 DAC Complementary

First semester (M2 - S1)

During this semester, there are 5 UE to choose from the table below.

Acronym Title Person(s) in charge ECTS Course
COCOMA Multiagent Coordination and Consensus: Models, Algorithms, Protocols Vincent Corruble 6 ANDROIDE
MAOA Scheduling Models, Algorithms and Applications Fanny Pascual 6 ANDROIDE
EVHI Highly Interactive Virtual Environments Vanda Luengo 6 ANDROIDE
MADI Models and Algorithms for Decision in Uncertainty Pierre-Henri Wuillemin 6 ANDROIDE
MOSIMA Multiagent Modeling and Simulation Jean-Daniel Kant 6 ANDROIDE
MADMC Models and Algorithms for Multicriteria and Collective Decision- Making Olivier Spanjaard 6 ANDROIDE
AOTJ Algorithms for Optimization and Game Theory Bruno Escoffier 6 ANDROIDE
ISG Serious Games Engineering Amel Yessad 6 ANDROIDE
IAR AI for Robotics Nicolas Bredeche 6 ANDROIDE

Second semester (M2 - S2)

The second semester is dedicated to an internship (company or laboratory).

Skills and Knowledge:

Upon completion, the graduate will be able to:

  • model optimization problems, and optimize algorithms for solving combinatorial problems and mathematical programs,
  • to collect and formalize expert knowledge and build decision support systems, particularly probabilistic ones,
  • to design computer tools to help a decision-maker analyze a problem or a situation and provide solutions,
  • to design and develop adaptive and autonomous agents or robots,
  • to design artificial intelligence algorithms for robotics, including navigation, mapping and planning, as well as learning and evolving algorithms for robot adaptation,
  • to design and produce intelligent interfaces, interactive environments, video games and serious games.


Target audience and prerequisites

This specialization is aimed at scientific students with a good knowledge of computer science and/or mathematics applied to computer science. The cohort of the first year is mainly made up of students with a bachelor's degree in computer science or mathematics (with some units in programming and algorithmic), but less typical backgrounds are also considered seriously. The second year cohort is made up of students from the first year, together with engineering students.

The prerequisites for our training are, on the one hand, to have general knowledge of computer science and a mastery of algorithms and programming (e.g. Java or C++) and, on the other hand, a strong grasp of basic mathematics (logic, algebra, analysis, probability, ...).


Responsible for the course

  • Aurélie Beynier - aurelie.beynier@sorbonne-universite.fr
  • Olivier Spanjaard - olivier.spanjaard@lip6.fr