Developing AI Agents with Simulated Data: Why, what, and how?
2026-02-17 • Artificial Intelligence
Artificial IntelligenceEmerging Technologies
AI summaryⓘ
The authors explain that one big problem for modern AI is not having enough good data to learn from. They talk about using simulations, which are computer-made virtual worlds, to create realistic synthetic data that AI can train on. The chapter covers the main ideas, advantages, and difficulties of using these simulated environments, especially digital twins, which are detailed virtual copies of real things. The authors also provide a framework to help build and study these simulation-based AI training tools.
synthetic datasubsymbolic AIsimulationdigital twinsAI trainingdata qualitydata volumevirtual environmentssynthetic data generation
Authors
Xiaoran Liu, Istvan David
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.