Physics-Based Task Generation through Causal Sequence of Physical Interactions

Chathura Gamage, Vimukthini Pinto, Matthew Stephenson, Jochen Renz

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)53-63
Number of pages11
JournalProceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE
Issue number1
Publication statusPublished - 6 Oct 2023
Event19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2023 - Salt Lake City, United States
Duration: 8 Oct 202312 Oct 2023


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


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