@inproceedings{AIIDE27501,
author = {Gamage, Chathura and Pinto, Vimukthini and Stephenson, Matthew and Renz, Jochen},
title = {Physics-based task generation through causal sequence of physical interactions},
year = {2023},
isbn = {1-57735-883-X},
publisher = {AAAI Press},
url = {https://doi.org/10.1609/aiide.v19i1.27501},
doi = {10.1609/aiide.v19i1.27501},
abstract = {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.},
booktitle = {Proceedings of the Nineteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment},
articleno = {6},
numpages = {11},
location = {Salt Lake City},
series = {AIIDE '23}
}