Katrien Beuls
Papers
2
Total Citations
13
H-Index
2
About
Katrien Beuls is a researcher working at the intersection of artificial intelligence, cognitive science, and computational linguistics, with a particular focus on emergent communication and grounded concept learning in autonomous agents. Her work addresses one of the fundamental challenges in AI: bridging the gap between raw sensori-motor data and the symbolic representations needed for reasoning and language. In her 2020 paper on discrimination-based strategies for grounded concept learning (9 citations), Beuls explores how agents can distill meaningful concepts from continuous observations — a critical step toward building machines capable of human-like communication. Complementing this, her practical guide to studying emergent communication through grounded language games (4 citations) has helped establish methodological foundations for researchers across artificial intelligence, linguistics, and statistical physics who study how effective communication systems can spontaneously arise in agent populations. Though her citation counts reflect the early stage of her published record, the interdisciplinary reach of her work — touching robotics, language evolution, and symbolic AI — signals growing influence. Beuls represents an emerging voice in the effort to build agents that can truly perceive, conceptualize, and communicate about their world.
Research Focus
Key Achievements
Top Papers
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