Artificial Intelligence, Algorithms, Interaction, Decision Making (AI2D)

The AI2D program 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.

Artificial Intelligence, Algorithms, Interaction, Decision Making (AI2D)

Objectives

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

 

Opportunities

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).

 

Program

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 AI2D Mandatory
LRC Logic and Knowledge Representations Jean-Gabriel Ganascia, Nicolas Maudet 6 AI2D/MIND Mandatory
MAPSI Probabilistic and Statistical Models and Algorithms for Computer Science   6 MIND/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  
PAI2D AI2D project Nicolas Bredeche, Nicolas Maudet 6 AI2D Mandatory
English English   3 Dept. Langues Mandatory
RA Robotics and Learning Olivier Sigaud 3 AI2D Mandatory
RP Problem Solving Evripidis Bampis 6 AI2D Complementary
FoSyMa Foundations of Multiagents Systes Aurélie Beynier 6 AI2D Complementary
IHM Human-Computer Interaction Gilles Bailly 6 AI2D Complementary
DJ Decision and Games Pierre-Henri Wuillemin 6 AI2D 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 AI2D
MAOA Scheduling Models, Algorithms and Applications Fanny Pascual 6 AI2D
EVHI Highly Interactive Virtual Environments Vanda Luengo 6 AI2D
MADI Models and Algorithms for Decision in Uncertainty Pierre-Henri Wuillemin 6 AI2D
MOSIMA Multiagent Modeling and Simulation Jean-Daniel Kant 6 AI2D
MADMC Models and Algorithms for Multicriteria and Collective Decision- Making Olivier Spanjaard 6 AI2D
AOTJ Algorithms for Optimization and Game Theory Bruno Escoffier 6 AI2D
ISG Serious Games Engineering Amel Yessad 6 AI2D
IAR AI for Robotics Nicolas Bredeche 6 AI2D

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, ...).

Contacts

Responsible for the course

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

Secretaries

sciences-master-info-AI2D@sorbonne-universite.fr