2 citations · 7 across the 14 of their papers we have counts for
6 papers · 1 filter
Harnessing Language for Coordination: A Framework and Benchmark for LLM-Driven Multi-Agent Control
Timothée Anne, Noah Syrkis, Meriem Elhosni +4
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. Their potential to facilitate human coordination with many agents is a promising but lar…
Collective Innovation in Groups of Large Language Models
Eleni Nisioti, Sebastian Risi, Ida Momennejad +2
Human culture relies on collective innovation: our ability to continuously explore how existing elements in our environment can be combined to create new ones. Language is hypothes…
Meta-Learning an Evolvable Developmental Encoding
Milton L. Montero, Erwan Plantec, Eleni Nisioti +2
Representations for black-box optimisation methods (such as evolutionary algorithms) are traditionally constructed using a delicate manual process. This is in contrast to the repre…
Evolving Self-Assembling Neural Networks: From Spontaneous Activity to Experience-Dependent Learning
Erwan Plantec, Joachin W. Pedersen, Milton L. Montero +2
Biological neural networks are characterized by their high degree of plasticity, a core property that enables the remarkable adaptability of natural organisms. Importantly, this ab…
Structurally Flexible Neural Networks: Evolving the Building Blocks for General Agents
Joachim Winther Pedersen, Erwan Plantec, Eleni Nisioti +2
Artificial neural networks used for reinforcement learning are structurally rigid, meaning that each optimized parameter of the network is tied to its specific placement in the net…
Growing Artificial Neural Networks for Control: the Role of Neuronal Diversity
Eleni Nisioti, Erwan Plantec, Milton Montero +2
In biological evolution complex neural structures grow from a handful of cellular ingredients. As genomes in nature are bounded in size, this complexity is achieved by a growth pro…