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Conference papers

An Algorithm for Self-Motivated Hierarchical Sequence Learning

Olivier L. Georgeon 1 Jonathan Morgan Frank Ritter
1 SILEX - Supporting Interaction and Learning by Experience
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This work demonstrates a mechanism that autonomously organizes an agent’s sequential behavior. The behavior organization is driven by pre-defined values associated with primitive behavioral patterns. The agent learns increasingly elaborated behaviors through its interactions with its environment. These learned behaviors are gradually organized in a hierarchy that reflects how the agent exploits the hierarchical regularities afforded by the environment. To an observer, the agent thus appears to exhibit basic self- motivated, sensible, and learning behavior to fulfill its inborn predilections. As such, this work illustrates Piaget’s theories of early-stage developmental learning.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01381588
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Friday, October 14, 2016 - 2:49:40 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:36 PM

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  • HAL Id : hal-01381588, version 1

Citation

Olivier L. Georgeon, Jonathan Morgan, Frank Ritter. An Algorithm for Self-Motivated Hierarchical Sequence Learning. International Conference on Cognitive Modeling, Aug 2010, Philadelphia, PA, United States. pp.73-78. ⟨hal-01381588⟩

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