The society of mind
AI planning: systems and techniques
AI Magazine
Intelligence without representation
Artificial Intelligence
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
An Behavior-based Robotics
Machine Learning
Multiple model-based reinforcement learning
Neural Computation
Intelligence by design: principles of modularity and coordination for engineering complex adaptive agents
On Intelligence
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Empirical Studies in Action Selection with Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive mixtures of local experts
Neural Computation
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Cognitive Systems Research
On the dynamics of robot exploration learning
Cognitive Systems Research
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Action selection (AS) is defined as the process where an action is selected among a number of alternatives. This definition, however, does not sufficiently describe what an action is. What is the unit of selection in the first place? We maintain that the artificial intelligence (AI) accounts of AS typically mix and merge two AS situations that indeed are qualitatively different. Most of the accounts actually deal only with one type of AS but purport to cover both types of AS. We propose three dimensions along which the commonalities and the differences between various AS accounts can be analyzed, and use these for a preliminary conceptualization of what we call a two-system action selection account. In particular, we identify two qualitatively different AS situations whose architectures, we suggest, can be designed inspired by neuroscience models of the basal ganglia (BG) and the cerebellum, respectively.