Multi-Agent Simulation for AI Behaviour Discovery in Operations Research
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 30 Aug 2021.
Builds on
4 of 28 references.
References · 4
- 2017Heuristic Online Goal Recognition in Continuous Domainscited by 11
- 2020OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generationcited by 2
- 2020Discrete-to-Deep Supervised Policy Learningcited by 1
- 2021Text Generation with Deep Variational GANcited by 1
Built on by
Citing papers per year: 2023: 1.
1 paper.
- 2023Diverse, Top-k, and Top-Quality Planning Over Simulatorscited by 3
- 2023Diverse, Top-k, and Top-Quality Planning Over Simulators25 Aug 2023