# Multi-Agent Simulation for AI Behaviour Discovery in Operations Research

arXiv: https://arxiv.org/abs/2108.13296 · HTML: https://mlsanity.com/paper/2108.13296/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Heuristic Online Goal Recognition in Continuous Domains](https://mlsanity.com/paper/1709.09839/) (2017)
- [OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation](https://mlsanity.com/paper/2004.07534/) (2020)
- [Discrete-to-Deep Supervised Policy Learning](https://mlsanity.com/paper/2005.02057/) (2020)
- [Text Generation with Deep Variational GAN](https://mlsanity.com/paper/2104.13488/) (2021)

## Built on by (most cited first, 1 of 1)

- [Diverse, Top-k, and Top-Quality Planning Over Simulators](https://mlsanity.com/paper/2308.13147/) (2023)

## Abstract (arXiv)

We describe ACE0, a lightweight platform for evaluating the suitability and viability of AI methods for behaviour discovery in multiagent simulations. Specifically, ACE0 was designed to explore AI methods for multi-agent simulations used in operations research studies related to new technologies such as autonomous aircraft. Simulation environments used in production are often high-fidelity, complex, require significant domain knowledge and as a result have high R&D costs. Minimal and lightweight simulation environments can help researchers and engineers evaluate the viability of new AI technologies for behaviour discovery in a more agile and potentially cost effective manner. In this paper we describe the motivation for the development of ACE0.We provide a technical overview of the system architecture, describe a case study of behaviour discovery in the aerospace domain, and provide a qualitative evaluation of the system. The evaluation includes a brief description of collaborative research projects with academic partners, exploring different AI behaviour discovery methods.