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AI Research
arXiv CS.AI·May 26·5 min read
Toward Enactive Artificial Intelligence
TL;DR
Mainstream artificial intelligence (AI) often overlooks the active, embodied nature of perception and cognition. This paper proposes integrating enactive principles, which emphasize experience and action-perception inseparability, into AI frameworks, particularly reinforcement learning (RL).
✦ Why It Matters
Engineers can enhance AI systems by integrating enactive principles for improved interaction and adaptability in real-world environments.
Key Takeaways
- Explore enactive principles to enhance AI perception models.
- Integrate action-perception inseparability in AI systems.
- Focus on autonomy in AI agent design.
- Consider embodiment in AI interactions with environments.
How It Works
Enactive approaches redefine perception as an active process where agents learn and adapt through their interactions with the environment. This contrasts with traditional models that view perception as a passive reception of data.
By emphasizing the role of experience and action, AI systems can be designed to better mimic human-like understanding and responsiveness.