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
Human-computer interaction: toward the year 2000
Human-computer interaction: toward the year 2000
Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
Neuromodulation of decision and response selection
Neural Networks - Computational models of neuromodulation
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Gibsonian Affordances for Roboticists
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
User interface affordances in a planning representation
Human-Computer Interaction
Valency for adaptive homeostatic agents: relating evolution and learning
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
An adaptive robot motivational system
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Hedonic value: enhancing adaptation for motivated agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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This article introduces a formalization of the dynamics between sensorimotor interaction and homeostasis, integrated in a single architecture to learn object affordances of consummatory behaviors. We also describe the principles necessary to learn grounded knowledge in the context of an agent and its surrounding environment, which we use to investigate the constraints imposed by the agentâ聙聶s internal dynamics and the environment. This is tested with an embodied, situated robot, in a simulated environment, yielding results that support this formalization. Furthermore, we show that this methodology allows learned affordances to be dynamically redefined, depending on object similarity, resource availability, and the rhythms of the agentâ聙聶s internal physiology. For example, if a resource becomes increasingly scarce, the value assigned by the agent to its related effect increases accordingly, encouraging a more active behavioral strategy to maintain physiological stability. Experimental results also suggest that a combination of motivation-driven and affordance learning in a single architecture should simplify its overall complexity while increasing its adaptivity.