Introduction to artificial intelligence
Introduction to artificial intelligence
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Self-organizing maps
Understanding intelligence
Computer science as empirical inquiry: symbols and search
Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Multiple model-based reinforcement learning
Neural Computation
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Accurate on-line support vector regression
Neural Computation
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
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We propose a novel approach that aims to realize autonomous developmental intelligence called Intelligence Dynamics. We emphasize two technical features of dynamics and embodiment in comparison with the symbolic approach of the conventional Artificial Intelligence. The essential conceptual idea of this approach is that an embodied agent interacts with the real world to learn and develop its intelligence as attractors of the dynamic interaction. We develop two computational models, one is for self-organizing multi-attractors, and the other provides a motivational system for open-ended learning agents. The former model is realized by recurrent neural networks with a small humanoid body in the real world, and the later is realized by hierarchical support vector machines with inverted pendulum agents in a virtual world. Although they are preliminary experiments, they take important first steps towards demonstrating the feasibility and value of open-ended learning agents with the concept of Intelligence Dynamics.