Physics-Based Task Generation through Causal Sequence of Physical Interactions
Chathura Gamage, Vimukthini Pinto, Matthew Stephenson, Jochen Renz

TL;DR
This paper introduces a systematic method for generating physics-based tasks from causal sequences of interactions, aiding in evaluating and developing AI systems' physical reasoning in simulated environments.
Contribution
The paper presents a novel approach to task generation in physics simulations using causal interaction sequences, enhancing the assessment of physical reasoning capabilities.
Findings
Successfully applied to Angry Birds puzzle game
Generated tasks evaluated for stability and solvability
Facilitates nuanced evaluation of physical reasoning agents
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…
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Taxonomy
TopicsArtificial Intelligence in Games · Topic Modeling · Multimodal Machine Learning Applications
