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Conference Papers Year : 2021

Grounding an Ecological Theory of Artificial Intelligence in Human Evolution

Eleni Nisioti
Katia Jodogne-del Litto
  • Function : Author
Clément Moulin-Frier

Abstract

Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. Although this ability is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes and ecological conditions that may have guided the emergence of complex cognitive capacities during the evolution of the species. Research in Human Behavioral Ecology (HBE) seeks to understand how the behaviors characterizing human nature can be conceived as adaptive responses to major changes in our ecological niche. In this paper, we propose a framework highlighting the role of environmental complexity in open-ended skill acquisition, grounded in major hypotheses from HBE and recent contributions in Reinforcement learning (RL). We use this framework to highlight fundamental links between the two disciplines, as well as to identify feedback loops that bootstrap ecological complexity and create promising research directions for AI researchers. We also present our first steps towards designing a simulation environment that implements the climate dynamics necessary for studying key HBE hypotheses relating environmental complexity to skill acquisition.
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Dates and versions

hal-03446961 , version 1 (26-11-2021)
hal-03446961 , version 2 (30-11-2021)

Identifiers

  • HAL Id : hal-03446961 , version 2

Cite

Eleni Nisioti, Katia Jodogne-del Litto, Clément Moulin-Frier. Grounding an Ecological Theory of Artificial Intelligence in Human Evolution. NeurIPS 2021 - Conference on Neural Information Processing Systems / Workshop: Ecological Theory of Reinforcement Learning, Dec 2021, virtual event, France. ⟨hal-03446961v2⟩
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