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

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: 27, pp. 1-24. doi:10.3389/frobt.2015.00027.

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Genewein, T1, 2, Author           
Leibfried, F1, 2, Author           
Grau-Moya, J1, 2, Author           
Braun, DA1, 2, Author           
Affiliations:
1Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497809              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Abstraction and hierarchical information-processing are hallmarks of human and animal intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving such a flexibility in artificial systems is challenging, even with more and more computational power. Here we investigate the hypothesis that abstraction and hierarchical information-processing might in fact be the consequence of limitations in information-processing power. In particular, we study an information-theoretic framework of bounded rational decision-making that trades off utility maximization against information-processing costs. We apply the basic principle of this framework to perception-action systems with multiple information-processing nodes and derive bounded optimal solutions. We show how the formation of abstractions and decision-making hierarchies depends on information-processing costs. We illustrate the theoretical ideas with example simulations and conclude by formalizing a mathematically unifying optimization principle that could potentially be extended to more complex systems.

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 Dates: 2015-10
 Publication Status: Published online
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 Identifiers: DOI: 10.3389/frobt.2015.00027
BibTex Citekey: GeneweinLGB2015
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Title: Frontiers in Robotics and AI
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Media
Pages: - Volume / Issue: 2 Sequence Number: 27 Start / End Page: 1 - 24 Identifier: ISSN: 2296-9144
CoNE: https://pure.mpg.de/cone/journals/resource/22969144