  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.
