The role of learning in autonomous robots
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Intelligent behaviour in animals and robots
Intelligent behaviour in animals and robots
Reinforcement learning for homeostatic endogenous variables
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Modeling motivations and emotions as a basis for intelligent behavior
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Multiple-goal reinforcement learning with modular Sarsa(O)
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Autonomous and fast robot learning through motivation
Robotics and Autonomous Systems
Linking perception and action through motivation and affect
Journal of Experimental & Theoretical Artificial Intelligence
The scared robot: motivations in a simulated robot arm
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Learning Affordances of Consummatory Behaviors: Motivation-Driven Adaptive Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Hedonic value: enhancing adaptation for motivated agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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We present a robot motivational system design framework The framework represents the underlying (possibly conflicting) goals of the robot as a set of drives, while ensuring comparable drive levels and providing a mechanism for drive priority adaptation during the robot's lifetime The resulting drive reward signals are compatible with existing reinforcement learning methods for balancing multiple reward functions We illustrate the framework with an experiment that demonstrates some of its benefits.