Life-inspired interoceptive artificial intelligence for autonomous and adaptive agents

Abstract

Despite remarkable advances in artificial intelligence (AI), building agents that can autonomously pursue goals while adapting to continuously changing environments remains a fundamental challenge. Living organisms naturally exhibit such adaptive autonomy, offering a compelling source of inspiration for how artificial systems might achieve similar capabilities. Here we focus on interoception—the process of monitoring and regulating internal bodily states to maintain internal homeostasis—which underwrites an organism’s survival. Developing interoceptive AI requires abstracting internal states from their biological instantiation into functional and mathematical representations that are applicable to artificial systems. This requires explicit factorization of state variables representing internal and external environments, together with mathematical formalization of life-inspired properties governing internal-state dynamics. Importantly, internal states can also function as universally available and intrinsically valuable contexts, serving as stable reference signals that modulate learning and behaviour under changing external environments. In addition, interoception-related biological processes, such as neuromodulatory mechanisms, can be incorporated to support context-dependent adaptive behaviour. Overall, this Perspective presents a unified account on how interoception can enhance autonomy and adaptivity in artificial agents by integrating insights from cybernetics with recent advances in theories of life, reinforcement learning and neuroscience. Lee, Oh et al. propose interoception as a biologically inspired framework for building more autonomous and adaptive AI agents, learning from living organisms to build autonomous and adaptive intelligence.