When robots weep: emotional memories and decision-making
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Emotion-driven learning for animat control
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Introduction to AI Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Asynchronous learning by emotions and cognition
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
A Robust Layered Control System For a Mobile Robot
A Robust Layered Control System For a Mobile Robot
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Neural networks for the EMOBOT robot control architecture
Neural Computing and Applications
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In this paper we describe an approach of controlling an autonomous robot by means of a hierarchical control structure, with a learning action selection. Since Damasio's "Descartes' error" in 1994 the number of approaches to action selection that use internal values, derived from psychological models of emotions or drives has increased significantly. The approach realises a learning action selection mechanism in a hierarchy of sensory and actuatory layers. The sensory values yield the internal states, as a basis for action selection. In addition they are used to calculate the reinforcement signal that trains the action selection.