Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Representing and Parameterizing Agent Behaviors
CA '02 Proceedings of the Computer Animation
Emotion and sociable humanoid robots
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Evaluating the Modeling and Use of Emotion in Virtual Humans
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Autonomous Robots: From Biological Inspiration to Implementation and Control (Intelligent Robotics and Autonomous Agents)
Autonomous biped gait pattern based on Fuzzy-CMAC neural networks
Integrated Computer-Aided Engineering
Towards human-like spoken dialogue systems
Speech Communication
Modularity and design in reactive intelligence
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Reinforcement distribution in continuous state action space fuzzy Q–learning: a novel approach
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A neural fuzzy inference based adaptive controller using learning process for nonholonomic robots
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
A cognitive approach for robots' vision using unsupervised learning and visual saliency
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Applied Computational Intelligence and Soft Computing - Special issue on Awareness Science and Engineering
Pattern Recognition Letters
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We propose a machine-learning based multi-level cognitive model inspired from early-ages' cognitive development of human's locomotion skills for humanoid robot's walking modeling. Contrary to the most of already introduced works dealing with biped robot's walking modeling, which place the problem within the context of controlling specific kinds of biped robots, the proposed model attends to a global concept of biped walking ability's construction independently from the robot to which the concept may be applied. The chief-benefit of the concept is that the issued machine-learning based structure takes advantage from ''learning'' capacity and ''generalization'' propensity of such models: allowing a precious potential to deal with high dimensionality, nonlinearity and empirical proprioceptive or exteroceptive information. Case studies and validation results are reported and discussed evaluating potential performances of the proposed approach.