MP: motion program synthesis with machine learning interpretability and knowledge graph analogy

Abstract The advancement of physics-based engines has led to the popularity of virtual reality. To achieve a more realistic and immersive user experience, the behaviours of objects in virtual scenes are expected to conform to real-world physical laws accurately. This increases the workload and development time for developers. To facilitate development on physics-based engines, this paper proposes MP that is a motion program synthesis approach based on machine learning and analogical reasoning. MP follows the paradigm of test-driven development, where programs are generated to fit test cases of motions subject to multiple environmental factors such as gravity and airflows. To reduce the search space of code generation, regression models are used to find variables that cause significant influences to motions, while analogical reasoning on knowledge graphs is used to find operators that work for the found variables. Besides, constraint solving is used to probabilistically estimate the values of constants in motion programs. Experimental results have demonstrated that MP is efficient in various motion program generation tasks, with random forest regressors achieving low data and time requirements.