@misc{rivera2020tanksworld,
author = {Rivera, Corban G. and Lyons, Olivia and Summitt, Arielle and Fatima, Ayman and Pak, Ji and Shao, William and Chalmers, Robert and Englander, Aryeh and Staley, Edward W. and Wang, I-Jeng and Llorens, Ashley J.},
title = {TanksWorld: A Multi-Agent Environment for AI Safety Research},
howpublished = {ArXiv preprint},
year = {2020},
month = {February},
abstract = {The ability to create artificial intelligence (AI) capable of performing complex tasks is rapidly outpacing our ability to ensure the safe and assured operation of AI-enabled systems. Fortunately, a landscape of AI safety research is emerging in response to this asymmetry and yet there is a long way to go. In particular, recent simulation environments created to illustrate AI safety risks are relatively simple or narrowly-focused on a particular issue. Hence, we see a critical need for AI safety research environments that abstract essential aspects of complex real-world applications. In this work, we introduce the AI safety TanksWorld as an environment for AI safety research with three essential aspects: competing performance objectives, human-machine teaming, and multi-agent competition. The AI safety TanksWorld aims to accelerate the advancement of safe multi-agent decision-making algorithms by providing a software framework to support competitions with both system performance and safety objectives. As a work in progress, this paper introduces our research objectives and learning environment with reference code and baseline performance metrics to follow in a future work.},
url = {http://approjects.co.za/?big=en-us/research/publication/tanksworld-a-multi-agent-environment-for-ai-safety-research/},
}