6 citations · 13 across the 22 of their papers we have counts for
24 papers
Continual Reinforcement Learning with Neuroevolution
Eleni Nisioti, Andrea Cossu, Kathrin Korte +1
Despite many studies about causes and remedies of plasticity loss in Reinforcement Learning (RL) under continual task changes, no RL method has yet consistently achieved a good bal…
Discovering Adaptive Transmission Programs for Collective Innovation
Cédric Colas, Jérémy Perez, Eleni Nisioti +4
Human collective intelligence depends on transmission processes: who shares what with whom, how, and when. While these processes emerge from individual cognition, they can also be…
Self-Organising Digital Circuits
Marcello Barylli, Gabriel Béna, Alexander Mordvintsev +2
Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes. Biological systems, in contrast, exhibit a…
Continual Evolution Strategies in Control Tasks
Nicola Pitzalis, Eleni Nisioti, Antonio Carta +2
We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones. On sequential MuJoCo locomotion tasks, naive E…
Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates
Elias Najarro, Ane Espeseth, Eleni Nisioti +2
Complexity and interpretability rarely coincide: systems rich enough for complex behaviours to emerge are usually too opaque to question, while transparent ones are too simple for…
Dimensionality Controls When Modularity Helps in Continual Learning
Kathrin Korte, Christian Medeiros Adriano, Joachim Winther Pedersen +2
Compositional learning systems must balance plasticity, the ability to acquire new knowledge, with stability, the preservation of previously learned components, especially when tas…
When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry
Kathrin Korte, Joachim Winter Pedersen, Eleni Nisioti +1
To preserve previously learned representations, continual learning systems must strike a balance between plasticity, the ability to acquire new knowledge, and stability. This stabi…
Hypernetworks That Evolve Themselves
Joachim Winther Pedersen, Erwan Plantec, Eleni Nisioti +4
How can neural networks evolve themselves without relying on external optimizers? We propose Self-Referential Graph HyperNetworks, systems where the very machinery of variation and…
When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?
Eleni Nisioti, Joachim Winther Pedersen, Erwan Plantec +2
The ability to continuously and efficiently transfer skills across tasks is a hallmark of biological intelligence and a long-standing goal in artificial systems. Reinforcement lear…
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…
From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models
Eleni Nisioti, Claire Glanois, Elias Najarro +7
Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we…
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…
Evolving Reservoirs for Meta Reinforcement Learning
Corentin Léger, Gautier Hamon, Eleni Nisioti +2
Animals often demonstrate a remarkable ability to adapt to their environments during their lifetime. They do so partly due to the evolution of morphological and neural structures.…
Emergence of Collective Open-Ended Exploration from Decentralized Meta-Reinforcement Learning
Richard Bornemann, Gautier Hamon, Eleni Nisioti +1
Recent works have proven that intricate cooperative behaviors can emerge in agents trained using meta reinforcement learning on open ended task distributions using self-play. While…
Dynamics of niche construction in adaptable populations evolving in diverse environments
Eleni Nisioti, Clément Moulin-Frier
In both natural and artificial studies, evolution is often seen as synonymous to natural selection. Individuals evolve under pressures set by environments that are either reset or…
Eco-evolutionary Dynamics of Non-episodic Neuroevolution in Large Multi-agent Environments
Gautier Hamon, Eleni Nisioti, Clément Moulin-Frier
Neuroevolution (NE) has recently proven a competitive alternative to learning by gradient descent in reinforcement learning tasks. However, the majority of NE methods and associate…
Autotelic Reinforcement Learning in Multi-Agent Environments
Eleni Nisioti, Elías Masquil, Gautier Hamon +1
In the intrinsically motivated skills acquisition problem, the agent is set in an environment without any pre-defined goals and needs to acquire an open-ended repertoire of skills.…
Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS
Eleni Nisioti, Mateo Mahaut, Pierre-Yves Oudeyer +2
Human culture relies on innovation: our ability to continuously explore how existing elements can be combined to create new ones. Innovation is not solitary, it relies on collectiv…
Plasticity and evolvability under environmental variability: the joint role of fitness-based selection and niche-limited competition
Eleni Nisioti, Clément Moulin-Frier
The diversity and quality of natural systems have been a puzzle and inspiration for communities studying artificial life. It is now widely admitted that the adaptation mechanisms e…
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations
Julius Taylor, Eleni Nisioti, Clément Moulin-Frier
Despite its rise as a prominent solution to the data inefficiency of today's machine learning models, self-supervised learning has yet to be studied from a purely multi-agent persp…
Grounding Artificial Intelligence in the Origins of Human Behavior
Eleni Nisioti, Clément Moulin-Frier
Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. However, although this ability is fundamentall…