Multi-Agent Simulation for AI Behaviour Discovery in Operations Research

• URL: http://arxiv.org/abs/2108.13296v1
• Date: Mon, 30 Aug 2021 15:14:06 GMT
• Title: Multi-Agent Simulation for AI Behaviour Discovery in Operations Research
• Authors: Michael Papasimeon, Lyndon Benke
• Abstract summary: ACE0 is a platform for evaluating the suitability and viability of AI methods for behaviour discovery in multiagent simulations. 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.
• Score: 0.9137554315375919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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