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  Abstraction in Decision-Makers with Limited Information Processing Capabilities

Genewein, T., & Braun, D. (2013). Abstraction in Decision-Makers with Limited Information Processing Capabilities. In NIPS 2013 Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games (pp. 1-9).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-001A-125D-D Version Permalink: https://hdl.handle.net/21.11116/0000-0001-4A06-6
Genre: Conference Paper

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NIPS-2013-Workshop-Genewein.pdf (Publisher version), 799KB
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Genewein, T1, 2, Author           
Braun, DA1, 2, Author           
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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: A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artificial systems abstractions are often implemented through computationally costly formations of groups or clusters. In this work we establish the relation between the free-energy framework for decision-making and rate-distortion theory and demonstrate how the application of rate-distortion for decision-making leads to the emergence of abstractions. We argue that abstractions are induced due to a limit in information processing capacity.

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 Dates: 2013-12
 Publication Status: Issued
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 Identifiers: BibTex Citekey: GeneweinB2013_3
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Title: NIPS 2013 Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games
Place of Event: Lake Tahoe, NV, USA
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Title: NIPS 2013 Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 9 Identifier: -