Grounding Artificial Intelligence in the Origins of Human Behavior
This paper tackles the problem of open-ended skill acquisition in AI by grounding it in the evolutionary origins of human behavior, offering a new conceptual framework for AI researchers.
This paper proposes a framework that links environmental complexity to open-ended skill acquisition in AI, drawing on Human Behavioral Ecology (HBE) and Reinforcement Learning (RL). It aims to understand how human-like intelligence, characterized by an open-ended repertoire of skills, could emerge by considering evolutionary processes.
Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. However, although this ability is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes 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 the structure of 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.