Developing AI Agents with Simulated Data: Why, what, and how?

Xiaoran Liu, Istvan David
2/17/2026
cs.AIcs.ET

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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.

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Cite this paper

@article{liu2026developing,
  title  = {Developing AI Agents with Simulated Data: Why, what, and how?},
  author = {Xiaoran Liu and Istvan David},
  year   = {2026},
  eprint = {2602.15816},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.15816v1}
}

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