TanksWorld: A Multi-Agent Environment for AI Safety Research. (arXiv:2002.11174v1 [cs.AI])

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.

Source link

Related posts

Kleiner Perkins and GV invest $21 million in AI health care startup – VentureBeat


Predicting and understanding the response to short-term intensive insulin therapy in people with early type 2 diabetes.


How Slaughter and May Is Making Use of Legal AI Across The Firm


This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy


COVID-19 (Coronavirus) is a new illness that is having a major effect on all businesses globally LIVE COVID-19 STATISTICS FOR World