20 citations · 38 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
Expedition & Expansion: Leveraging Semantic Representations for Goal-Directed Exploration in Continuous Cellular Automata
Sina Khajehabdollahi, Gautier Hamon, Marko Cvjetko +3
Discovering diverse visual patterns in continuous cellular automata (CA) is challenging due to the vastness and redundancy of high-dimensional behavioral spaces. Traditional explor…
WorldLLM: Improving LLMs' world modeling using curiosity-driven theory-making
Guillaume Levy, Cedric Colas, Pierre-Yves Oudeyer +2
Large Language Models (LLMs) possess general world knowledge but often struggle to generate precise predictions in structured, domain-specific contexts such as simulations. These l…
MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces
Loris Gaven, Thomas Carta, Clément Romac +4
Open-ended learning agents must efficiently prioritize goals in vast possibility spaces, focusing on those that maximize learning progress (LP). When such autotelic exploration is…
A Definition of Open-Ended Learning Problems for Goal-Conditioned Agents
Olivier Sigaud, Gianluca Baldassarre, Cedric Colas +5
A lot of recent machine learning research papers have ``open-ended learning'' in their title. But very few of them attempt to define what they mean when using the term. Even worse,…
Augmenting Autotelic Agents with Large Language Models
Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer +2
Humans learn to master open-ended repertoires of skills by imagining and practicing their own goals. This autotelic learning process, literally the pursuit of self-generated (auto)…