Epistemic Adaptation in Self-Evolving Machine Learning Systems Under Open-World Constraints
Keywords:
Epistemic adaptation, open-world learning, self-evolving AI, continual learning, model uncertainty, knowledge graphs, lifelong learning, agentic AIAbstract
Epistemic adaptation represents a transformative frontier in machine learning where systems evolve not only their parameters but also their ontological understanding of the world. In open-world environments, characterized by unpredictable inputs, changing tasks, and unbounded knowledge domains, traditional supervised learning models are insufficient. Self-evolving systems empowered by epistemic reasoning can navigate uncertainty, integrate novel information, and restructure their internal representations. This paper explores the mechanisms, challenges, and architectures required for epistemic adaptation under open-world constraints, emphasizing continual learning, uncertainty estimation, knowledge plasticity, and dynamic model evolution.
References
Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. Proceedings of the 33rd International Conference on Machine Learning (ICML), 48, 1050–1059.
Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in Neural Information Processing Systems, 30, 6402–6413.
Van de Ven, G. M., & Tolias, A. S. (2018). Three scenarios for continual learning. Neural Information Processing Systems Workshops, 31, 1–9.
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., ... & Hadsell, R. (2016). Progressive neural networks. arXiv preprint arXiv:1606.04671, 1(1), 1–14.
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71.
Al-Shedivat, M., Finn, C., Bansal, S., Genewein, T., Abbeel, P., & Levine, S. (2018). Continuous adaptation via meta-learning in nonstationary and competitive environments. International Conference on Learning Representations (ICLR), 2018, 1–14.
Mundt, M., Hong, Y., Pliushch, I., & Ramesh, V. (2020). A wholistic view of continual learning with deep neural networks: Forgotten lessons and the future. Neural Networks, 123, 337–357.
Ramapuram, J., Gregorova, M., & Kalousis, A. (2017). Lifelong generative modeling. British Machine Vision Conference (BMVC), 2017, 1–12.
Farquhar, S., Osborne, M. A., & Gal, Y. (2018). Towards robust evaluations of continual learning. Neural Information Processing Systems Workshops, 2018, 1–10.
Chen, Z., & Liu, B. (2018). Lifelong machine learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 12(3), 1–207.
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565, 1(1), 1–29.
Ghosh, A., Rajeswaran, A., & Levine, S. (2021). Generalized task inference in continual learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021(1), 200–209.
Henry, R. J., & Ferrer, C. C. (2023). Epistemic modeling in autonomous systems: Knowledge-aware agents in open environments. Artificial Intelligence Review, 56(6), 4312–4331.
