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[2602.15816] Developing AI Agents with Simulated Data: Why, what, and how?
Summary
This article discusses the significance of synthetic data generation through simulation for training AI agents, addressing challenges and benefits in the field.
Why It Matters
As AI continues to evolve, the need for high-quality training data becomes critical. This article highlights how simulation can effectively generate diverse synthetic data, which is essential for improving AI performance and adoption. Understanding these methods can help researchers and practitioners overcome data limitations.
Key Takeaways
- Synthetic data generation is vital for AI training due to data quality issues.
- Simulation provides a systematic approach to creating diverse datasets.
- The article outlines key concepts, benefits, and challenges of simulation-based data generation.
- A reference framework is introduced for designing digital twin-based AI solutions.
- Understanding these methods can enhance AI development and deployment.
Computer Science > Artificial Intelligence arXiv:2602.15816 (cs) [Submitted on 17 Feb 2026] Title:Developing AI Agents with Simulated Data: Why, what, and how? Authors:Xiaoran Liu, Istvan David View a PDF of the paper titled Developing AI Agents with Simulated Data: Why, what, and how?, by Xiaoran Liu and 1 other authors View PDF HTML (experimental) Abstract:As insufficient data volume and quality remain the key impediments to the adoption of modern subsymbolic AI, techniques of synthetic data generation are in high demand. Simulation offers an apt, systematic approach to generating diverse synthetic data. This chapter introduces the reader to the key concepts, benefits, and challenges of simulation-based synthetic data generation for AI training purposes, and to a reference framework to describe, design, and analyze digital twin-based AI simulation solutions. Subjects: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) Cite as: arXiv:2602.15816 [cs.AI] (or arXiv:2602.15816v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.15816 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Istvan David [view email] [v1] Tue, 17 Feb 2026 18:53:27 UTC (275 KB) Full-text links: Access Paper: View a PDF of the paper titled Developing AI Agents with Simulated Data: Why, what, and how?, by Xiaoran Liu and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | n...