Katrien Beuls
À propos
Biographie
My research programme revolves around the question of how a population of autonomous agents can self-organize conceptual and linguistic systems that allow them to communicate and reason in their native environment. I study this question using computational models that simulate the emergence and evolution of such systems in multi-agent systems. In these experiments, agents have a range of cognitive abilities at different levels. First, the sensory-motor level implements an interface between the real world and the models of the world constructed by agents. Then, the conceptual level allows agents to reason about their models of the world and to make decisions on the actions to be performed. Finally, the linguistic level allows agents to exchange information through a shared language. I am an active co-developer of advanced software modules that address each of these levels, available to the research community at https://www.emergent-languages.org.
Selected publications
- Beuls, K., & Van Eecke, P. (2024). Humans learn language from situated communicative interactions. What about machines? Computational Linguistics, doi: https://doi.org/10.1162/coli_a_00534
- Doumen, J., Beuls, K., & Van Eecke, P. (2024). Modelling constructivist language acquisition through syntactico-semantic pattern finding. Royal Society Open Science, 11(7), 231998. https://doi.org/10.1098/rsos.231998
Facultés/Départements/Services
Instituts de recherche
Organes
Domaines d'expertises
- Computational linguistics
- Computational construction grammar
- Natural language understanding
- Neuro-symbolic artificial intelligence
- Multi-agent systems
- Emergent communication
2024-2025
-
Actualités en data science [IDASM211]
-
Bases de données et modélisation [INFCB311]
-
Machine learning [IHDCM037]
-
Machine learning and data mining [ICYBM101]
-
Natural language processing [INFOM233]
-
Projet en data analytics [IDASM104]
-
Techniques d'interaction et de visualisation [INFCB211]
2023-2024
-
Actualités en data science [IDASM211]
-
Bases de données et modélisation [INFCB311]
-
Machine learning [IHDCM037]
-
Machine learning : des réseaux de neurones aux big data [ICYBM101]
-
Natural language processing [INFOM233]
-
Projet en data analytics [IDASM104]
-
Techniques d'interaction et de visualisation [INFCB211]
2022-2023
-
Bases de données et modélisation [INFCB311]
-
Machine learning [IHDCM037]
-
Projet en data analytics [IDASM104]