Novelty Generation Framework for AI Agents in Angry Birds Style Physics Games

Handling novel situations is a critical capability of Artificial Intelligence (AI) agents when working in open-world physical environments. To develop and evaluate these agents, we need realistic and meaningful novelties, that is, novelties that are detectable and learnable. However, there is a lack of research in the area of creating novelties for AI agents in physical environments. Physics-based video games are popular among AI researchers due to the ability to create realistic and controllable physical environments. In this paper, we present a systematic novelty generation framework for physics-based video games. This framework allows the user to define a specific objective when generating novel content that ensures detectability. We instantiate the proposed framework for the video game Angry Birds and conduct experiments to show that the generated novel content is consistent with the user-defined objectives. Furthermore, we use a reinforcement learning agent to experiment with the learnability of the generated novel content.