We provide conceptual clues for one promising Artificial Life (ALife) route to Artificial Intelligence (AI) based on the notion of habit. We draw from an enactive approach that considers habits as the building blocks for mental life and, consequently, as the foundation for a science of mind. By taking this standpoint, this approach departs from the conventional view of intelligence in AI, which is based on “higher-order” cognitive functions. The first part of the paper addresses the idea of taking habits as the foundation for modeling intelligent behavior. This requires us to consider the so-called “scaling up” problem and rethink the concept of intelligence that still pervades in mainstream cognitive science. In the second part, we present the enactive approach to habits, emphasizing their adaptive and complex nature, as well as their fundamental role in guiding behavior. Finally, we acknowledge some limitations in the current enactive models of habits: either they are disembodied and decoupled, but allow for a rich landscape of attractors, or they are embodied and coupled, but remain too minimal. We propose a bridge between existing models and point to the need to go beyond the individual to include a social domain. We conclude that to better model intelligent behavior, embodied and situated agents must be capable of developing an increasingly complex network of habits from which an intelligent self emerges.

This content is only available as a PDF.