Multi-Agent Simulation for AI Behaviour Discovery in Operations Research
This work addresses the problem of reducing R&D costs for researchers and engineers in operations research, but it is incremental as it builds on existing simulation and AI methods.
The authors tackled the high cost and complexity of using high-fidelity simulations for AI behavior discovery in operations research by developing ACE0, a lightweight platform that enables agile and cost-effective evaluation of AI methods, as demonstrated through a case study in aerospace.
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.