Fountas, Zafeirios

Deep active inference agents using Monte-Carlo methods

Fountas, Z., Sajid, N., Mediano, P. A. M., & Friston, K. (2020) · arXiv

Embodied and Extended CognitionReinforcement Learning in Robotics

Cited by 64

Exploration and preference satisfaction trade-off in reward-free learning

Sajid, N., Tigas, P., Zakharov, A., Fountas, Z., & Friston, K. (2021) · arXiv

Reinforcement Learning in RoboticsEmbodied and Extended Cognition

Cited by 11

Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning

Yamada, J., Shawe-Taylor, J., & Fountas, Z. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN)

Mathematical and Theoretical Epidemiology and Ecology ModelsReinforcement Learning in Robotics

Cited by 11

Multimodal Data Fusion based on the Global Workspace Theory

Bao, C., Fountas, Z., Olugbade, T., & Bianchi-Berthouze, N. (2020) · arXiv

Multimodal Machine Learning ApplicationsEmotion and Mood Recognition

Cited by 9

Human-inspired Episodic Memory for Infinite Context LLMs

Fountas, Z., Benfeghoul, M. A., Oomerjee, A., Christopoulou, F., Lampouras, G., Bou-Ammar, H., & Wang, J. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 3