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

TL;DR

This paper discusses the importance of simulation-based synthetic data generation for AI training, highlighting its benefits, challenges, and a framework for designing digital twin-based solutions to overcome data limitations.

Contribution

It introduces a comprehensive reference framework for designing and analyzing digital twin-based simulation solutions for synthetic data generation in AI.

Findings

Simulation effectively generates diverse synthetic data for AI training.

Digital twin-based approaches address data quality and volume issues.

Framework aids in systematic design and analysis of simulation solutions.

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.