A menu of designs for reinforcement learning over time
Neural networks for control
A possibility for implementing curiosity and boredom in model-building neural controllers
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
Conscious robot that distinguishes between self and others and implements imitation behavior
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Levels and Types of Action Selection: The Action Selection Soup
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Learning robot-environment interaction using echo state networks
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
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In this paper, the processes of exploration and of incremental learning in the robot navigation task are studied using the dynamical systems approach. A neural network model which performs the forward modeling, planning, consolidation learning and novelty rewarding is used for the robot experiments. Our experiments showed that the robot repeated a few variations of travel patterns in the beginning of the exploration, and later the robot explored more diversely in the workspace by combining and mutating the previously experienced patterns. Our analysis indicates that internal confusion due to immature learning plays the role of a catalyst in generating diverse action sequences. It is found that these diverse exploratory travels enable the robot to acquire adequate modeling of the environment in the end.