Energentic Intelligence: From Self-Sustaining Systems to Enduring Artificial Life
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912415297306624 |
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| author | Karagoz, Atahan |
| author_facet | Karagoz, Atahan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energentic Intelligence: From Self-Sustaining Systems to Enduring Artificial Life Karagoz, Atahan Artificial Intelligence Machine Learning Systems and Control 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. |
| title | Energentic Intelligence: From Self-Sustaining Systems to Enduring Artificial Life |
| topic | Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2506.04916 |