Bounded rationality, abstraction and hierarchical decision-making: an 
information-theoretic optimality principle

Genewein, T; Leibfried, F; Grau-Moya, J; Braun, DA

doi:10.3389/frobt.2015.00027

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Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle

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Genewein, T
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Intelligent Systems, Max Planck Society;
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Leibfried, F
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Intelligent Systems, Max Planck Society;
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Grau-Moya, J
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Intelligent Systems, Max Planck Society;
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83827

Braun, DA
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Intelligent Systems, Max Planck Society;
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Genewein, T., Leibfried, F., Grau-Moya, J., & Braun, D. (2015). Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle. Frontiers in Robotics and AI, 2:. doi:10.3389/frobt.2015.00027.

引用: https://hdl.handle.net/11858/00-001M-0000-002A-556D-7

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