Descriptif
effectifs minimal / maximal:
10/12Diplôme(s) concerné(s)
Parcours de rattachement
Format des notes
Numérique sur 20Littérale/grade réduitPour les étudiants du diplôme Echange international non diplomant
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 3 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Diplôme d'ingénieur
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 3 ECTS
- Crédit d'UE électives acquis : 3
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Data & Artificial Intelligence
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
Programme détaillé
An ant colony can find the shortest path in a complex environment; a species can solve complex adaptation problems; economic agents may spontaneously reach a locally optimal allocation of resources. Simple individual acts, in each case, produce non-trivial results at the collective level.
These observations constitute a rich source of inspiration for innovative engineering solutions, such as optimization using genetic algorithms, or message routing in telecom networks.
The emergent behaviour of complex collective systems often goes against intuition. Its dynamics can be described through non-linear models that predict sudden transitions. Emergence is best apparent during those transitions. Its study consists in accounting for the appearance of collective patterns when individual, generally simple, behaviours are given as input.
The main techniques studied in this module are:
- Genetic algorithms, in which a virtual population evolves and collectively adapts to a particular problem or to a new environment.
- Swarm intelligence, as a model of natural phenomena and as a class of collective algorithms. They are used to address problems in which adaptability and robustness are essential.
- Emergence of phenomena like morphogenesis, cooperation, segregation through symmetry breaking, and emergence in social networks. We show how these different models can be applied to concrete problems, such as message routing in communication networks, optimal resource allocation or the emergence of communication.
The notion of emergence is formally defined, as well as concepts like punctuated equilibria, scale invariance, implicit parallelism and autocatalytic phenomena.