Physics-Based Novel Task Generation through Disrupting and Constructing Causal Interactions

In response to the growing demand for AI systems that can operate the physical world, there has been an increasing interest in enhancing their physical reasoning capabilities. Equally crucial is the ability to handle unseen novel situations, as such situations frequently arise in real-world environments. To facilitate the development of AI systems with those abilities, researchers have developed testbeds with specialized tasks to evaluate agents' adaptation to novelty in physical environments. In this paper, we propose a method for generating physics-based tasks with incorporated novelties to assess agents' novelty adaptation capabilities. The tasks are defined as causal sequences of physical interactions between objects, and novelties are strategically introduced to disrupt existing causal relationships and construct new ones. This approach ensures that agents must adapt to the effects of novelties to perform those tasks, enabling confident measurement of their novelty adaptation capabilities using task performance. Moreover, our methodology eliminates the need for manual task creation, unlike existing novelty-centric testbeds. The proposed method is demonstrated and evaluated using 12 physical scenarios in the Angry Birds domain. The evaluated metrics include generation time, physical stability, intended solvability, intended unsolvability, and accidental solvability of the tasks, and they yielded favourable results compared to the literature.