# AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation

arXiv: https://arxiv.org/abs/2203.08994 · HTML: https://mlsanity.com/paper/2203.08994/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 73 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Using Hindsight to Anchor Past Knowledge in Continual Learning](https://mlsanity.com/paper/2002.08165/) (2020)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning](https://mlsanity.com/paper/2112.02706/) (2021)
- [Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks](https://mlsanity.com/paper/2112.10017/) (2021)
- [Open-world Learning and Application to Product Classification](https://mlsanity.com/paper/1809.06004/) (2018)
- [Open-world Machine Learning: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2105.13448/) (2021)
- [CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions](https://mlsanity.com/paper/2009.09929/) (2020)
- [Interactive Open-Ended Object, Affordance and Grasp Learning for Robotic Manipulation](https://mlsanity.com/paper/1904.02530/) (2019)
- [Intrinsic motivations and open-ended learning](https://mlsanity.com/paper/1912.13263/) (2019)
- [Lifelong and Interactive Learning of Factual Knowledge in Dialogues](https://mlsanity.com/paper/1907.13295/) (2019)
- [Data-Driven Hierarchical Predictive Learning in Unknown Environments](https://mlsanity.com/paper/2005.05948/) (2020)

## Built on by (most cited first, 23 of 23)

- [Recent Advances of Foundation Language Models-based Continual Learning: A Survey](https://mlsanity.com/paper/2405.18653/) (2024)
- [Fully Autonomous AI Agents Should Not be Developed](https://mlsanity.com/paper/2502.02649/) (2025)
- [Learnability and Algorithm for Continual Learning](https://mlsanity.com/paper/2306.12646/) (2023)
- [Open-World Continual Learning: Unifying Novelty Detection and Continual Learning](https://mlsanity.com/paper/2304.10038/) (2023)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [Position: Foundation Agents as the Paradigm Shift for Decision Making](https://mlsanity.com/paper/2405.17009/) (2024)
- [CLoG: Benchmarking Continual Learning of Image Generation Models](https://mlsanity.com/paper/2406.04584/) (2024)
- [A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds](https://mlsanity.com/paper/2306.06272/) (2023)
- [Curiosity-Driven Imagination: Discovering Plan Operators and Learning Associated Policies for Open-World Adaptation](https://mlsanity.com/paper/2503.04931/) (2025)
- [AI Must not be Fully Autonomous](https://mlsanity.com/paper/2507.23330/) (2025)
- [Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence](https://mlsanity.com/paper/2407.03652/) (2024)
- [Text Classification Under Class Distribution Shift: A Survey](https://mlsanity.com/paper/2502.12965/) (2025)
- [Improving Open-world Continual Learning under the Constraints of Scarce Labeled Data](https://mlsanity.com/paper/2502.20974/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [A cybersecurity AI agent selection and decision support framework](https://mlsanity.com/paper/2510.01751/) (2025)
- [Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Direction](https://mlsanity.com/paper/2510.15422/) (2025)
- [Learning After Model Deployment](https://mlsanity.com/paper/2510.17160/) (2025)
- [What's the next frontier for Data-centric AI? Data Savvy Agents](https://mlsanity.com/paper/2511.01015/) (2025)
- [MOSLD-Bench: Multilingual Open-Set Learning and Discovery Benchmark for Text Categorization](https://mlsanity.com/paper/2601.13437/) (2026)
- [Breaking Task Impasses Quickly: Adaptive Neuro-Symbolic Learning for Open-World Robotics](https://mlsanity.com/paper/2601.16985/) (2026)
- [MAND: Modality-Aware Novelty Detection for Open-World Egocentric Activity Recognition](https://mlsanity.com/paper/2603.16970/) (2026)
- [HyperDCM: Dynamic Cluster Memory Replay in Hyperbolic Space for Continual Robotic Navigation Across Scenes](https://mlsanity.com/paper/2607.16267/) (2026)
- [NeuCME: Toward Dynamic Multimodal Continual Learning via Neural Combinatorics of Multiple Experts](https://mlsanity.com/paper/2609.07009/) (2026)

## Abstract (arXiv)

As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances. As the real-world is an open environment that is full of unknowns or novelties, the capabilities of detecting novelties, characterizing them, accommodating/adapting to them, gathering ground-truth training data and incrementally learning the unknowns/novelties become critical in making the AI agent more and more knowledgeable, powerful and self-sustainable over time. The key challenge here is how to automate the process so that it is carried out continually on the agent's own initiative and through its own interactions with humans, other agents and the environment just like human on-the-job learning. This paper proposes a framework (called SOLA) for this learning paradigm to promote the research of building autonomous and continual learning enabled AI agents. To show feasibility, an implemented agent is also described.