6 ECTS credits
150 u studietijd
Aanbieding 2 met studiegidsnummer 4013088ENR voor alle studenten in het 1e semester met een verdiepend master niveau.
The aim of the course is to introduce the students to the field of Multi-agent learning and learning in populations of agents.
He or she will learn the basic principles of both domains, the mathematical and computational methods and the typical problems they are trying to solve.
The students will also obtain a basic understanding of (evolutionary) game theory which will allow them to understand the standard literature in that field and the relevance of this domain to learning in general.
The students will obtain the skills to address independently problems within these fields.
In addition, they will be capable of presenting their work to an audience of specialists and non-specialists.
The course addresses two general areas of research : individual-based learning and social learning in populations.
The first part provides an introduction to the principles of learning by imitation, modeled through evolutionary dynamics. It will explain what evolution is and how games can be used to model interactions between individuals in a population. For this purpose basic concepts of Game Theory are introduced. It will show how these models can be used to study the evolution of cooperation in social dilemmas, the evolution of conventions like language or even the dynamics of cancer.
The second part focusses on learning through experience, of which reinforcement learning is the standard example. We start from a reacp of simple single agent reinforcement learning (stateless RL) which we extent to deal with the interplay of multiple learning agents in the same environment.
The course concludes with a project where students in small teams have to reproduce the reults of a scientific paper, discus and extend the results.
Course schedule, course material and information on assignments and project: http://www.ulb.ac.be/di/map/tlenaert/Home_Tom_Lenaerts/INFO-F-409.html
Knowledge and insight
The student knows different Reinforcement Learning (RL) techniques. The student can judge different exploration strategies, is knowledgeable about the exploration/exploration trade off. The student has insight in the dynamics of multiagent systems and reinforcement learning and the relevancy of the basic concepts of Game Theory in this context.
Application of knowledge and insight
The student can solve and avaluate a concrete problem using a multiagent reinforcement learning technique or evolutionary game theory and its dynamics.
Judgment ability
The student can give arguments why a given problem is suited or not to be apporached through RL and evolutionary dynamics.
Communication
The students must complete 3 written assignments and 1 project in a team of 3 ot 4 students that needs to be presented.
Skills
The student will be prepared to read autonomously the literature In this research domain. The students will obtain the skills to address independently problems within these fields. In addition, they will be capable of presenting their work to an audience of specialists and non-specialists.
De beoordeling bestaat uit volgende opdrachtcategorieën:
WPO Praktijkopdracht bepaalt 50% van het eindcijfer
ZELF Paper bepaalt 50% van het eindcijfer
Binnen de categorie WPO Praktijkopdracht dient men volgende opdrachten af te werken:
Binnen de categorie ZELF Paper dient men volgende opdrachten af te werken:
The final score will be based on the active participation of the student during the lectures and in particular the efforts and results delivered for the assignments (including assignments on game theory and the discussion of a paper). The assignments need to be completed during the semester of teaching.
Deze aanbieding maakt deel uit van de volgende studieplannen:
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Artificiële Intelligentie
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Multimedia
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Software Languages and Software Engineering
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Data Management en Analytics
Master in Applied Sciences and Engineering: Computer Science: Artificial Intelligence (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Multimedia (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Data Management and Analytics (enkel aangeboden in het Engels)
Educatieve master in de wetenschappen en technologie: computerwetenschappen (120 ECTS, Etterbeek)