Generating Novelty in Open-World Multi-Agent Strategic Board Games
This addresses the problem of AI robustness in open-world environments for researchers and developers, but it is incremental as it builds on existing multi-agent and novelty adaptation concepts.
The authors introduced GNOME, a platform for testing multi-agent AI systems' ability to handle unanticipated novelty in strategic board games like Monopoly, as demonstrated at NeurIPS 2020 and used in the DARPA SAIL-ON program.
We describe GNOME (Generating Novelty in Open-world Multi-agent Environments), an experimental platform that is designed to test the effectiveness of multi-agent AI systems when faced with \emph{novelty}. GNOME separates the development of AI gameplaying agents with the simulator, allowing \emph{unanticipated} novelty (in essence, novelty that is not subject to model-selection bias). Using a Web GUI, GNOME was recently demonstrated at NeurIPS 2020 using the game of Monopoly to foster an open discussion on AI robustness and the nature of novelty in real-world environments. In this article, we further detail the key elements of the demonstration, and also provide an overview of the experimental design that is being currently used in the DARPA Science of Artificial Intelligence and Learning for Open-World Novelty (SAIL-ON) program to evaluate external teams developing novelty-adaptive gameplaying agents.