Life-inspired 'interoceptive AI' framework gives agents internal states for autonomous decision-making
CurieuxExplorer · x · 2026-09-05
A team at Korea's Institute for Basic Science, led by Associate Director Woo Choong-Wan, proposed an "interoceptive AI" framework inspired by how organisms monitor and regulate internal conditions.
- Core idea: Agents get explicitly defined internal states (satiation, hydration, body temperature, damage) that serve as context for learning and decision-making, not just signals to monitor
- Formalization: The researchers mathematically defined how internal and external states can be separated yet interact, and how maintaining internal stability shapes rewards and behavior
- Experiments: In a 3D virtual survival environment, agents sense the world via vision, olfaction, temperature, and collision while tracking four internal variables; training spans four increasingly complex levels with resources, obstacles, and predators, with generalization tested in unseen environments
Peer-reviewed publication.
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