Multi-Agent Simulation for AI Behaviour Discovery in Operations Research
Abstract
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.
Cite
@article{arxiv.2108.13296,
title = {Multi-Agent Simulation for AI Behaviour Discovery in Operations Research},
author = {Michael Papasimeon and Lyndon Benke},
journal= {arXiv preprint arXiv:2108.13296},
year = {2021}
}
Comments
14 pages, 7 figures. To be published in proceedings of the 22nd International Workshop on Multi-Agent-Based Simulation (MABS 2021) at AAMAS 2021. https://mabsworkshop.github.io/accepted/