Energentic Intelligence: From Self-Sustaining Systems to Enduring Artificial Life
This work addresses the challenge of deploying autonomous agents in resource-volatile settings where persistence must be self-regulated, offering a foundational approach for enduring artificial life.
The paper tackles the problem of creating autonomous systems that can sustain themselves through internal energy regulation, rather than task performance, and demonstrates in simulation that this approach leads to stable, resource-aware behavior without external supervision.
This paper introduces Energentic Intelligence, a class of autonomous systems defined not by task performance, but by their capacity to sustain themselves through internal energy regulation. Departing from conventional reward-driven paradigms, these agents treat survival-maintaining functional operation under fluctuating energetic and thermal conditions-as the central objective. We formalize this principle through an energy-based utility function and a viability-constrained survival horizon, and propose a modular architecture that integrates energy harvesting, thermal regulation, and adaptive computation into a closed-loop control system. A simulated environment demonstrates the emergence of stable, resource-aware behavior without external supervision. Together, these contributions provide a theoretical and architectural foundation for deploying autonomous agents in resource-volatile settings where persistence must be self-regulated and infrastructure cannot be assumed.