A bottom-up mechanism for behavior selection in an artificial creature
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Explorations in evolutionary robotics
Adaptive Behavior
Evolving action selection and selective attention without actions, attention, or selection
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Being There: Putting Brain, Body, and World Together Again
Being There: Putting Brain, Body, and World Together Again
Toward Spinozist Robotics: Exploring the Minimal Dynamics of Behavioral Preference
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Associative Learning on a Continuum in Evolved Dynamical Neural Networks
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
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The dynamical systems approach in the cognitive and behavioral sciences studies how systems made of many coupled components across brain, body, and environment self-organize to generate behavior. This approach has mostly focused on models of single actions and has not addressed how a dynamical system can engage in multiple different directed actions. In this paper, we introduce a family of artificial life models that demonstrate how dynamical agents can engage in multiple different actions and autonomously switch between them. These described agents engage in a food foraging task, and are driven by both internal, metabolic variables and external, sensory variables. The analysis of one of these agents demonstrates how different actions can arise through transient modes of sensorimotor coordination, in which a subset of the available sensors and effectors become engaged while others are ignored. Transitions between actions are analyzed and shown to correspond to rapid movements through the agent's state space. In these transitions, some of the previously controlling sensors and effectors disengage, and new sets of sensors and effectors are engaged.