Physics-Based Task Generation through Causal Sequence of Physical Interactions


  • Chathura Gamage The Australian National University
  • Vimukthini Pinto The Australian National University
  • Matthew Stephenson Flinders University
  • Jochen Renz The Australian National University



Physics-Based Tasks, Physical Reasoning, Content Generation, AI For Level Generation, Physics Puzzles Generation, Angry Birds


Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications.




How to Cite

Gamage, C., Pinto, V., Stephenson, M., & Renz, J. (2023). Physics-Based Task Generation through Causal Sequence of Physical Interactions. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1), 53-63.