Papers
2
Total Citations
19
H-Index
2
About
Pierre De Loor is a pioneering researcher at the intersection of artificial intelligence and cognitive science, whose work redefines how machines learn and interact with humans. His primary research areas include enactive AI, associative learning, and human-machine coevolution. De Loor’s most significant contribution is his advocacy for enaction-based artificial intelligence, which challenges traditional computational models by emphasizing embodied, dynamic cognition and the co-construction of meaning between humans and machines. His seminal 2013 paper, "Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop" (17 citations), argues for a paradigm shift where AI systems evolve through continuous, reciprocal interaction with human users, rather than static programming. In his 2011 work, "Guiding for Associative Learning: How to Shape Artificial Dynamic Cognition" (2 citations), De Loor explores how to steer artificial systems toward adaptive, context-sensitive learning, mirroring biological cognition. Though his citation counts are modest, his ideas are foundational for researchers in cognitive robotics and human-centered AI, offering a radical vision for machines that learn not from data alone, but from lived, relational experience. De Loor’s work inspires a new generation of AI that is co-creative, adaptive, and deeply human.
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