Grounding Artificial Intelligence in the Origins of Human Behavior

TL;DR

This paper proposes a framework linking environmental complexity, human behavioral ecology, and reinforcement learning to understand how ecological factors influence open-ended skill acquisition in AI, inspired by human evolution.

Contribution

It introduces a novel framework that integrates ecological complexity and human behavioral insights with reinforcement learning to guide AI development.

Findings

Highlights the role of environmental complexity in skill acquisition.

Identifies feedback loops between ecological factors and cognitive development.

Suggests new research directions combining ecology, human behavior, and AI.

Abstract

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.