Information Theoretic Incentives for Cognitive Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (30 June 2015) | Viewed by 65241
Special Issue Editors
Interests: Intrinsic Motivation (Empowerment); Self-Organization; Guided Self-Organization; Information-Theoretic Incentives for Social Interaction; Information-Theoretic Incentives for Swarms; Information Theory and Computer Game AI
Interests: Autonomous Robots; Self-Organization; Guided Self-Organization; Information Theory; Dynamical Systems; Machine Learning; Neuroscience of Learning; Optimal Control
Interests: Embodied Artificial Intelligence; Information Theory of the Sensorimotor Loop; Dynamical Systems; Cybernetics; Self-organisation; Synaptic plasticity; Evolutionary Robotics
Interests: artificial life; artificial intelligence; information theory; minimally cognitive agents; embodiment
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Special Issue Information
Dear Colleagues,
In recent years, ideas such as "life is information processing" or "information holds the key to understanding life" have become more common. However, how can information, or more formally Information Theory, increase our understanding of life, or life-like systems?
Information Theory not only has a profound mathematical basis, but also typically provides an intuitive understanding of processes, such as learning, behavior and evolution terms of information processing.
In this special issue, we are interested in both: a.) the information-theoretic formalization and quantification of different aspects of life, such as driving forces of learning and behavior generation, information flows between neurons, swarm members and social agents, and information theoretic aspects of evolution and adaptation and b.) the simulation and creation of life-like systems with previously identified principles and incentives.
Topics with relation to artificial and natural systems:
- information theoretic intrinsic motivations
- information theoretic quantification of behavior
- information theoretic guidance of artificial evolution
- information theoretic guidance of self-organization
- information theoretic driving forces behind learning
- information theoretic driving forces behind behavior
- information theory in swarms
- information theory in social behavior
- information theory in evolution
- information theory in the brain
- information theory in system-environment distinction
- information theory in the perception action loop
- information theoretic definitions of life
Dr. Christoph Salge
Dr. Georg Martius
Dr. Keyan Ghazi-Zahedi
Dr. Daniel Polani
Guest Editors
[1] A. S. Klyubin, D. Polani, and C.L. Nehaniv. Empowerment: A Universal Agent-Centric Measure of Control. In Proc. CEC 2005.
[2] G. Martius, R. Der, and N. Ay. Information driven self-organization of complex robotic behaviors. PLoS ONE, 8(5):e63400, 2013.
[3] D. Y. Little, F. T. Sommer: Learning and exploration in action-perception loops. Frontiers in Neural Circuits, 2013
[4] Max Lungarella, Olaf Sporns, Information self-structuring: key principle for learning and development. Proc. ICDL, 2005.
[5] Polani, Daniel. "Information: currency of life?." HFSP journal 3.5 (2009): 307-316.
[6] K. Zahedi, N. Ay, and R. Der. Higher coordination with less control – a result of information maximization in the sensori-motor loop. Adaptive Behavior, 18(3–4):338–355, 2010.
[7] K. Zahedi and N. Ay. Quantifying morphological computation. Entropy, 15(5):1887–1915, 2013.
[8] R. Pfeifer, M. Lungarella, and F. Iida. Self-organization, embodiment, and biologically inspired robotics. Science, 318(5853):1088–1093, 2007.
[9] Salge, Christoph, Cornelius Glackin, and Daniel Polani. "Changing the Environment Based on Empowerment as Intrinsic Motivation." Entropy 16.5 (2014): 2789-2819.
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