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Conference Papers Year : 2016

Modeling Neuromodulation as a Framework to Integrate Uncertainty in General Cognitive Architectures

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Abstract

One of the most critical properties of a versatile intelligent agent is its capacity to adapt autonomously to any change in the environment without overly complexifying its cognitive architecture. In this paper, we propose that understanding the role of neuromodulation in the brain is of central interest for this purpose. More precisely, we propose that an accurate estimation of the nature of uncertainty present in the environment is performed by specific brain regions and broadcast throughout the cerebral network by neuromodulators, resulting in appropriate changes in cerebral functioning and learning modes. Better understanding the principles of these mechanisms in the brain might tremendously inspire the field of Artificial General Intelligence. The original contribution of this paper is to relate the four major neuromodulators to four fundamental dimensions of uncertainty.
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Dates and versions

hal-01342902 , version 1 (07-07-2016)

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Frédéric Alexandre, Maxime Carrere. Modeling Neuromodulation as a Framework to Integrate Uncertainty in General Cognitive Architectures. The Ninth Conference on Artificial General Intelligence, Jul 2016, New-York, United States. ⟨10.1007/978-3-319-41649-6_33⟩. ⟨hal-01342902⟩
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