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Generating Natural Behaviors using Constructivist Algorithms

Abstract : We present a project to design interactive devices (smart displays, robots, etc.) capable of self-motivated learning through non-goal-directed interactive behaviors (e.g., curious, emotional, playful behaviors). We use and improve algorithms inspired by constructivist epistemology that we have designed previously. These algorithms incrementally learn sequential hierarchies of control loops in a bottom-up and open-ended fashion, and continuously reuse the learned higher-level control loops to generate increasingly complex behaviors that exhibit self-motivation. This project contributes to research in self-supervised learning because the learning is driven by low-level preferences that under-determine the device’s future behaviors, leaving room for individuation, which, in turn, opens the way to autonomy in learning.
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Contributor : Olivier Georgeon Connect in order to contact the contributor
Submitted on : Friday, September 4, 2020 - 11:35:20 AM
Last modification on : Friday, January 21, 2022 - 4:43:50 PM


  • HAL Id : hal-02930211, version 1


Olivier L. Georgeon, Paul Robertson, Jianyong Xue. Generating Natural Behaviors using Constructivist Algorithms. International Workshop on Self-Supervised Learning, Feb 2020, Boston, United States. pp.5-14. ⟨hal-02930211⟩



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