Toward Enactive Artificial Intelligence
This is a conceptual position paper for AI researchers, proposing a shift in theoretical foundations, but it is incremental as it builds on existing enactive philosophy and RL parallels without presenting new empirical results.
The paper advocates for integrating enactive approaches to perception and cognition into AI, identifying four key concepts (experience, action-perception inseparability, autonomy, embodiment) and arguing that reinforcement learning partially aligns with these principles but lacks key elements. It suggests broader incorporation of enactive ideas into AI and RL.
In this paper, we advocate for incorporating enactive approaches to perception and cognition into artificial intelligence (AI). Enactive approaches view perception as an active, skillful engagement with the world, where agents perceive by acting and by understanding how their actions shape their experience. This contrasts with classical views that treat perception as a passive internal process in which the brain receives sensory input, processes it, and issues commands for action. Enactive views emphasize the dynamic, embodied, and interactive character of perception, grounded in the lived experience of agents embedded in their environments. We identify and develop four key enactive concepts that we find most relevant to AI: experience, action perception inseparability, autonomy, and embodiment. Much of mainstream AI, from classical rule based systems to large language models, has largely neglected these insights, treating cognition as internal processing detached from embodied interaction and intrinsic normativity. Reinforcement learning (RL), however, exhibits structural resonance with enactive principles through its emphasis on action, agent environment interaction, feedback driven adaptation, and agent centered evaluation. However, this resonance should not be taken as theoretical equivalence, as RL approximates some enactive insights, but key elements remain absent or weakly developed. Building on this analysis, we suggest a broader incorporation of enactive ideas into both mainstream AI and RL.