cs.AIFeb 17, 2026
SaveDeveloping AI Agents with Simulated Data: Why, what, and how?
Organizations: McMaster University, Canada · McSCert, McMaster University, Canada
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 & tables
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
| AI training | |
|---|---|
| A Query | {Implicit, Explicit} |
| B Sim. data volume | {Big data , Small data} |
| A-B Training fashion | {Batch, Live} |
| Data collection | |
| C Observe | {Passive observation, Active experimentation} |
| D Data | {Stationary historical data, Low-context data update, High-context data update} |
Table 1: Variation points in the DT4AI framework
Figure 4: AI patterns (relevant components highlighted)
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