Search Results for author:
Found 28 papers, 14 papers with code
Cultural evolution in populations of Large Language Models
We here present a framework for simulating cultural evolution in populations of LLMs, allowing the manipulation of variables known to be important in cultural evolution, such as network structure, personality, and the way social information is aggregated and transformed.
Discovering Sensorimotor Agency in Cellular Automata using Diversity Search
The research field of Artificial Life studies how life-like phenomena such as autopoiesis, agency, or self-regulation can self-organize in computer simulations.
Evolving Reservoirs for Meta Reinforcement Learning
At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL).
Meta-Diversity Search in Complex Systems, A Recipe for Artificial Open-Endedness ?
Holmes incrementally learns a hierarchy of modular representations to characterize divergent sources of diversity and uses a goal-based intrinsically-motivated exploration as the diversity search strategy.
Emergence of Collective Open-Ended Exploration from Decentralized Meta-Reinforcement Learning
We further find that the agents learned collective exploration strategies extend to an open ended task setting, allowing them to solve task trees of twice the depth compared to the ones seen during training.
SBMLtoODEjax: Efficient Simulation and Optimization of Biological Network Models in JAX
Advances in bioengineering and biomedicine demand a deep understanding of the dynamic behavior of biological systems, ranging from protein pathways to complex cellular processes.
Dynamics of niche construction in adaptable populations evolving in diverse environments
In this work, we study NC in simulation environments that consist of multiple, diverse niches and populations that evolve their plasticity, evolvability and niche-constructing behaviors.
Eco-evolutionary Dynamics of Non-episodic Neuroevolution in Large Multi-agent Environments
Neuroevolution (NE) has recently proven a competitive alternative to learning by gradient descent in reinforcement learning tasks.
Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization
Finally, we show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized, allowing for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules.
Cooperative control of environmental extremes by artificial intelligent agents
This includes major innovations that allowed to reduce and control the impact of extreme events.
Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communication
In this paper, we investigate whether artificial agents can develop a shared language in an ecological setting where communication relies on a sensory-motor channel.
Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS
Comparing the level of innovation achieved by different social network structures across different tasks shows that, first, consistent with human findings, experience sharing within a dynamic structure achieves the highest level of innovation in tasks with a deceptive nature and large search spaces.
Cultural Vocal Bursts Intensity Prediction
Reinforcement Learning (RL)
Language and Culture Internalisation for Human-Like Autotelic AI
Building autonomous agents able to grow open-ended repertoires of skills across their lives is a fundamental goal of artificial intelligence (AI).
Plasticity and evolvability under environmental variability: the joint role of fitness-based selection and niche-limited competition
In this work, we study the interplay between environmental dynamics and adaptation in a minimal model of the evolution of plasticity and evolvability.
Learning to Guide and to Be Guided in the Architect-Builder Problem
ABIG results in a low-level, high-frequency, guiding communication protocol that not only enables an architect-builder pair to solve the task at hand, but that can also generalize to unseen tasks.
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations
In this work, we propose that aligning internal subjective representations, which naturally arise in a multi-agent setup where agents receive partial observations of the same underlying environmental state, can lead to more data-efficient representations.
Grounding Spatio-Temporal Language with Transformers
While there is an extended literature studying how machines can learn grounded language, the topic of how to learn spatio-temporal linguistic concepts is still largely uncharted.
Grounding Artificial Intelligence in the Origins of Human Behavior
Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills.
Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc).
Language-Goal Imagination to Foster Creative Exploration in Deep RL
We introduce the Playground environment and study how this form of goal imagination improves generalization and exploration over agents lacking this capacity.
Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges
Computational models of emergent communication in agent populations are currently gaining interest in the machine learning community due to recent advances in Multi-Agent Reinforcement Learning (MARL).
BIG-bench Machine Learning
Multi-agent Reinforcement Learning
+3
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot.
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment.
Decision Making under Uncertainty: A Quasimetric Approach
Schematically, two main approaches have been followed: either the agent learns which option is the correct one to choose in a given situation by trial and error, or the agent already has some knowledge on the possible consequences of his decisions; this knowledge being generally expressed as a conditional probability distribution.
Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed
The core tenet of the model is that the listener uses hypotheses about the word the speaker is currently uttering to update probabilities linking the sound produced by the speaker to phonemes in the native language repertoire of the listener.
