Martin Biehl
Martin Biehl
Cross Labs, Cross Compass
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A strategy for origins of life research
C Scharf, N Virgo, HJ Cleaves, M Aono, N Aubert-Kato, A Aydinoglu, ...
Astrobiology 15 (12), 1031-1042, 2015
Interaction learning for dynamic movement primitives used in cooperative robotic tasks
T Kulvicius, M Biehl, MJ Aein, M Tamosiunaite, F Wörgötter
Robotics and Autonomous Systems 61 (12), 1450-1459, 2013
Information closure theory of consciousness
AYC Chang, M Biehl, Y Yu, R Kanai
Frontiers in Psychology 11, 1504, 2020
Information generation as a functional basis of consciousness
R Kanai, A Chang, Y Yu, I Magrans de Abril, M Biehl, N Guttenberg
Neuroscience of consciousness 2019 (1), niz016, 2019
A technical critique of some parts of the free energy principle
M Biehl, FA Pollock, R Kanai
Entropy 23 (3), 293, 2021
Expanding the active inference landscape: more intrinsic motivations in the perception-action loop
M Biehl, C Guckelsberger, C Salge, SC Smith, D Polani
Frontiers in neurorobotics, 45, 2018
Towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems
M Biehl, T Ikegami, D Polani
Proceedings of the Artificial Life Conference 2016, 722, 2016
Modular robot control environment testing neural control on simulated and real robots
F Hesse, G Martius, P Manoonpong, M Biehl, F Wörgötter
Frontiers in Computational Neuroscience, Conference Abstract: Bernstein …, 2012
Causal blankets: Theory and algorithmic framework
FE Rosas, PAM Mediano, M Biehl, S Chandaria, D Polani
International Workshop on Active Inference, 187-198, 2020
Action and perception for spatiotemporal patterns
M Biehl, D Polani
14th European Conference on Artificial Life 2017 14, 68-75, 2017
Formal approaches to a definition of agents
M Biehl
arXiv preprint arXiv:1704.02716, 2017
Dynamics of a bayesian hyperparameter in a markov chain
M Biehl, R Kanai
International Workshop on Active Inference, 35-41, 2020
Geometry of Friston's active inference
M Biehl
arXiv preprint arXiv:1811.08241, 2018
Learning body-affordances to simplify action spaces
N Guttenberg, M Biehl, R Kanai
arXiv preprint arXiv:1708.04391, 2017
Specific and complete local integration of patterns in Bayesian networks
M Biehl, T Ikegami, D Polani
Entropy 19 (5), 230, 2017
Interpreting Dynamical Systems as Bayesian Reasoners
N Virgo, M Biehl, S McGregor
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021
Investigating Transformational Complexity: Counting Functions a Region Induces on Another in Elementary Cellular Automata
M Biehl, O Witkowski
Complexity 2021, 2021
Free energy, empowerment, and predictive information compared
M Biehl, C Guckelsberger, C Salge, SC Smith, D Polani
The Ninth International Conference on Guided Self-Organisation (GSO-2018 …, 2018
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks
N Guttenberg, M Biehl, R Kanai
arXiv:1609.00116 [cs.AI], 2016
Apparent actions and apparent goal-directedness
M Biehl, D Polani
Proceedings of the European Conference on Artificial Life 2015 (ECAL 2015 …, 2015
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