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

TL;DR

The chapter addresses data scarcity and quality in AI training by advocating simulation-based synthetic data generation, including four main simulation modalities and the use of digital twins. It articulates the benefits of simulation—cost, speed, and controllability—while detailing challenges such as the sim-to-real gap, data validation, and privacy concerns. A central contribution is the DT4AI framework, which formalizes interactions among the AI agent, a high-fidelity Digital Twin, and the Physical Twin to enable safe, targeted data generation and training. The work highlights the practical significance of digital twins for high-fidelity AI simulation and outlines future directions that combine advances in generative AI with DT-enabled training, emphasizing cross-domain collaboration and standardized architectures.

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.

Figures (4)

• Figure 1: Schematic overview of AI training data generation by simulation
• Figure 2: Typical data generation techniques for AI training
• Figure 3: The DT4AI framework
• Figure 4: AI patterns (relevant components highlighted)