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Mehdi Khamassi
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2020 – today
- 2024
- [c25]Paris Oikonomou, Athanasios Dometios, Mehdi Khamassi, Costas S. Tzafestas:
Online Prediction of Novel Trajectories Using a Library of Movement Primitives. ICDL 2024: 1-8 - [i11]Gianluca Baldassarre, Richard J. Duro, Emilio Cartoni, Mehdi Khamassi, Alejandro Romero, Vieri Giuliano Santucci:
Purpose for Open-Ended Learning Robots: A Computational Taxonomy, Definition, and Operationalisation. CoRR abs/2403.02514 (2024) - [i10]Kathinka Evers, Michele Farisco, Raja Chatila, B. D. Earp, Ismael T. Freire, F. Hamker, E. Nemeth, Paul F. M. J. Verschure, Mehdi Khamassi:
Artificial consciousness. Some logical and conceptual preliminaries. CoRR abs/2403.20177 (2024) - [i9]Mehdi Khamassi, Marceau Nahon, Raja Chatila:
Strong and weak alignment of large language models with human values. CoRR abs/2408.04655 (2024) - 2023
- [j20]Mehdi Khamassi
, Marco Mirolli, Christian Wallraven:
Editorial: Neurorobotics explores the human senses. Frontiers Neurorobotics 17 (2023) - [j19]Paris Oikonomou, Athanasios Dometios, Mehdi Khamassi, Costas S. Tzafestas:
Zero-shot model-free learning of periodic movements for a bio-inspired soft-robotic arm. Frontiers Robotics AI 10 (2023) - [j18]Rémi Dromnelle
Reducing Computational Cost During Robot Navigation and Human-Robot Interaction with a Human-Inspired Reinforcement Learning Architecture. Int. J. Soc. Robotics 15(8): 1297-1323 (2023) - [j17]Gilles Bailly, Mehdi Khamassi
Computational Model of the Transition from Novice to Expert Interaction Techniques. ACM Trans. Comput. Hum. Interact. 30(5): 66:1-66:33 (2023) - 2022
- [j16]Elisa Massi, Jeanne Barthélemy
Model-Based and Model-Free Replay Mechanisms for Reinforcement Learning in Neurorobotics. Frontiers Neurorobotics 16 (2022) - [j15]Erwan Renaudo, Philipp Zech
Editorial: Computational models of affordance for robotics. Frontiers Neurorobotics 16 (2022) - [c24]Paris Oikonomou, Athanasios Dometios, Mehdi Khamassi, Costas S. Tzafestas:
Reproduction of Human Demonstrations with a Soft-Robotic Arm based on a Library of Learned Probabilistic Movement Primitives. ICRA 2022: 5212-5218 - 2021
- [c23]Iñaki Rañó, Mehdi Khamassi, KongFatt Wong-Lin:
Stability Analysis of Bio-inspired Source Seeking with Noisy Sensors. ECC 2021: 341-346 - [c22]Paris Oikonomou, Athanasios Dometios
Task Driven Skill Learning in a Soft-Robotic Arm. IROS 2021: 1716-1723 - 2020
- [j14]Mehdi Khamassi
Modeling awake hippocampal reactivations with model-based bidirectional search. Biol. Cybern. 114(2): 231-248 (2020) - [j13]Mariacarla Staffa
Special Issue on Behavior Adaptation, Interaction, and Artificial Perception for Assistive Robotics. Int. J. Soc. Robotics 12(3): 613-616 (2020) - [j12]Abolfazl Zaraki
A Novel Reinforcement-Based Paradigm for Children to Teach the Humanoid Kaspar Robot. Int. J. Soc. Robotics 12(3): 709-720 (2020) - [c21]Paris Oikonomou, Mehdi Khamassi
Periodic movement learning in a soft-robotic arm*. ICRA 2020: 4586-4592 - [c20]Rémi Dromnelle
How to Reduce Computation Time While Sparing Performance During Robot Navigation? A Neuro-Inspired Architecture for Autonomous Shifting Between Model-Based and Model-Free Learning. Living Machines 2020: 68-79 - [c19]Rémi Dromnelle
Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies. RO-MAN 2020: 612-617 - [c18]Mehdi Khamassi
Adaptive Coordination of Multiple Learning Strategies in Brains and Robots. TPNC 2020: 3-22 - [p1]Frédéric Alexandre, Peter F. Dominey, Philippe Gaussier, Benoît Girard, Mehdi Khamassi, Nicolas P. Rougier:
When Artificial Intelligence and Computational Neuroscience Meet. A Guided Tour of Artificial Intelligence Research (3) (III) 2020: 303-335 - [i8]Rémi Dromnelle, Erwan Renaudo, Guillaume Pourcel, Raja Chatila, Benoît Girard, Mehdi Khamassi:
How to reduce computation time while sparing performance during robot navigation? A neuro-inspired architecture for autonomous shifting between model-based and model-free learning. CoRR abs/2004.14698 (2020) - [i7]Rémi Dromnelle, Benoît Girard, Erwan Renaudo, Raja Chatila, Mehdi Khamassi:
Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies. CoRR abs/2005.03987 (2020) - [i6]Stéphane Doncieux, Nicolas Bredèche, Leni K. Le Goff, Benoît Girard, Alexandre Coninx, Olivier Sigaud, Mehdi Khamassi, Natalia Díaz Rodríguez, David Filliat, Timothy M. Hospedales, A. E. Eiben, Richard J. Duro:
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics. CoRR abs/2005.06223 (2020)
2010 – 2019
- 2019
- [c17]Jack Hadfield, Georgia Chalvatzaki, Petros Koutras, Mehdi Khamassi, Costas S. Tzafestas, Petros Maragos:
A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention Task. IROS 2019: 1251-1256 - [c16]James Gillespie
Using Reinforcement Learning to Attenuate for Stochasticity in Robot Navigation Controllers. SSCI 2019: 705-713 - 2018
- [j11]Raja Chatila
Toward Self-Aware Robots. Frontiers Robotics AI 5: 88 (2018) - [j10]Nassim Aklil, Benoît Girard
Sequential Action Selection and Active Sensing for Budgeted Localization in Robot Navigation. Int. J. Semantic Comput. 12(1): 109-128 (2018) - [j9]George Velentzas, Theodore Tsitsimis, Iñaki Rañó
Adaptive reinforcement learning with active state-specific exploration for engagement maximization during simulated child-robot interaction. Paladyn J. Behav. Robotics 9(1): 235-253 (2018) - [j8]Laurent Dollé, Ricardo Chavarriaga, Agnès Guillot, Mehdi Khamassi
Interactions of spatial strategies producing generalization gradient and blocking: A computational approach. PLoS Comput. Biol. 14(4) (2018) - [j7]Mehdi Khamassi
Robot Fast Adaptation to Changes in Human Engagement During Simulated Dynamic Social Interaction With Active Exploration in Parameterized Reinforcement Learning. IEEE Trans. Cogn. Dev. Syst. 10(4): 881-893 (2018) - [c15]Ouriel Grynszpan, Esther Mouquet, Matthew F. S. Rushworth, Jérôme Sallet, Mehdi Khamassi:
Computational Model of the User's Learning Process When Cued by a Social Versus Non-Social Agent. HAI 2018: 347-349 - [c14]Lise Aubin, Mehdi Khamassi
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays. Living Machines 2018: 16-27 - [c13]Mehdi Khamassi, Georgia Chalvatzaki, Theodore Tsitsimis, George Velentzas, Costas S. Tzafestas:
A Framework for Robot Learning During Child-Robot Interaction with Human Engagement as Reward Signal. RO-MAN 2018: 461-464 - [i5]Lise Aubin, Mehdi Khamassi, Benoît Girard:
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays. CoRR abs/1802.05594 (2018) - [i4]Jack Hadfield, Georgia Chalvatzaki, Petros Koutras, Mehdi Khamassi, Costas S. Tzafestas, Petros Maragos:
A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task. CoRR abs/1812.00253 (2018) - 2017
- [j6]Nicolas P. Rougier
Sustainable computational science: the ReScience initiative. PeerJ Comput. Sci. 3: e142 (2017) - [c12]Iñaki Rañó
A drift diffusion model of biological source seeking for mobile robots. ICRA 2017: 3525-3531 - [c11]Mehdi Khamassi, George Velentzas, Theodore Tsitsimis, Costas S. Tzafestas
Active Exploration and Parameterized Reinforcement Learning Applied to a Simulated Human-Robot Interaction Task. IRC 2017: 28-35 - [c10]Nassim Aklil, Benoît Girard
Sequential Action Selection for Budgeted Localization in Robots. IRC 2017: 97-100 - [c9]James Gillespie
Reinforcement Learning for Bio-Inspired Target Seeking. TAROS 2017: 637-650 - [i3]Nicolas P. Rougier
Sustainable computational science: the ReScience initiative. CoRR abs/1707.04393 (2017) - [i2]Guillaume Viejo, Benoît Girard, Emmanuel Procyk, Mehdi Khamassi:
Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving task. CoRR abs/1711.00698 (2017) - 2016
- [i1]Mehdi Khamassi, Costas S. Tzafestas:
Active exploration in parameterized reinforcement learning. CoRR abs/1610.01986 (2016) - 2015
- [c8]Erwan Renaudo
Respective Advantages and Disadvantages of Model-based and Model-free Reinforcement Learning in a Robotics Neuro-inspired Cognitive Architecture. BICA 2015: 178-184 - [c7]Erwan Renaudo
Which criteria for autonomously shifting between goal-directed and habitual behaviors in robots? ICDL-EPIROB 2015: 254-260 - 2014
- [j5]Florian Lesaint, Olivier Sigaud, Shelly B. Flagel, Terry E. Robinson, Mehdi Khamassi
Modelling Individual Differences in the Form of Pavlovian Conditioned Approach Responses: A Dual Learning Systems Approach with Factored Representations. PLoS Comput. Biol. 10(2) (2014) - [c6]Erwan Renaudo
Design of a Control Architecture for Habit Learning in Robots. Living Machines 2014: 249-260 - 2012
- [c5]Ken Caluwaerts, Antoine Favre-Félix, Mariacarla Staffa
Neuro-inspired Navigation Strategies Shifting for Robots: Integration of a Multiple Landmark Taxon Strategy. Living Machines 2012: 62-73 - [c4]Jean Bellot, Olivier Sigaud, Mehdi Khamassi
Which Temporal Difference Learning Algorithm Best Reproduces Dopamine Activity in a Multi-choice Task? SAB 2012: 289-298 - 2011
- [j4]Mehdi Khamassi
Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model. Frontiers Neurorobotics 5: 1 (2011) - 2010
- [j3]Adrien Peyrache
Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution. J. Comput. Neurosci. 29(1-2): 309-325 (2010) - [c3]Mehdi Khamassi
A Computational Model of Integration between Reinforcement Learning and Task Monitoring in the Prefrontal Cortex. SAB 2010: 424-434
2000 – 2009
- 2008
- [c2]Laurent Dollé, Mehdi Khamassi
Analyzing Interactions between Navigation Strategies Using a Computational Model of Action Selection. Spatial Cognition 2008: 71-86 - 2007
- [b1]Mehdi Khamassi:
Complementary roles of the rat prefrontal cortex and striatum in reward-based learning and shifting navigation strategies. (Rôles complémentaires du cortex préfrontal et du striatum dans l'apprentissage et le changement de stratégies de navigation basées sur la récompense chez le rat). Pierre and Marie Curie University, Paris, France, 2007 - 2006
- [c1]Mehdi Khamassi, Louis-Emmanuel Martinet, Agnès Guillot:
Combining Self-organizing Maps with Mixtures of Experts: Application to an Actor-Critic Model of Reinforcement Learning in the Basal Ganglia. SAB 2006: 394-405 - 2005
- [j2]Mehdi Khamassi
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats. Adapt. Behav. 13(2): 131-148 (2005) - [j1]Jean-Arcady Meyer, Agnès Guillot, Benoît Girard
The Psikharpax project: towards building an artificial rat. Robotics Auton. Syst. 50(4): 211-223 (2005)
Coauthor Index
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last updated on 2024-10-07 22:16 CEST by the dblp team
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