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Resonance Thinking and Inductive Machine Learning

Abstract : This paper presents one of the symbiotic parts deemed necessary to complete our theory of computer systems design devoted to incomplete domains, here called Cartesian Systemic Emergence (CSE). CSE is dealing with one version of the concept of (semi)-automated creativity. We call systems implementing this kind of creativity Symbiotic Recursive Pulsative Systems (SRPS). SRPS are intended to contribute to solving real-world problems in incomplete domains requiring control and prevention. CSE is concerned with strategic aspects of the conception of such SPRS. Each component of a SPRS has to be symbiotically linked to all the other components. This requirement is not very usual in Computer Science, hence we have to introduce notions that are not yet present in scientific vocabulary. This paper is devoted to the most important features of one particular way of thinking present in CSE. We call it ‘Resonance Thinking’ (RT). RT takes care of generating and handling experiments during CSE. We explain that RT causes the complexity of CSE to be analogous to Ackermann’s function computation complexity.
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Contributor : Marta Franova Connect in order to contact the contributor
Submitted on : Sunday, July 21, 2019 - 10:30:57 AM
Last modification on : Saturday, June 25, 2022 - 10:38:27 PM


  • HAL Id : hal-02190008, version 1


Marta Franova, yves Kodratoff. Resonance Thinking and Inductive Machine Learning. ICONS 2019, The Fourteenth International Conference on Systems, ISBN: 978-1-61208-696-5, Mar 2019, Valencia, Spain. pp.7-13. ⟨hal-02190008⟩



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