@inproceedings{241715c04f6a40fc930f0d4ad8bcc903,
title = "Towards explainable inference about object motion using qualitative reasoning",
abstract = "The capability of making explainable inferences regarding physical processes has long been desired. One fundamental physical process is object motion. Inferring what causes the motion of a group of objects can even be a challenging task for experts, e.g., in forensic science. Most of the work in the literature rely on physics simulation to draw such inferences. The simulation requires a precise model of the underlying domain to work well and is essentially a black-box from which one can hardly obtain any useful explanation. By contrast, qualitative reasoning methods have the advantage in making transparent inferences with ambiguous information, which makes it suitable for this task. However, there has been no suitable qualitative theory proposed for object motion in three-dimensional space. We take this challenge and develop a qualitative theory for the motion of rigid objects. Based on this theory, we develop a reasoning method to solve a very interesting problem: Assuming there are several objects that were initially at rest and now have started to move. We want to infer what action causes the movement of these objects.",
author = "Xiaoyu Ge and Jochen Renz and Hua Hua",
note = "Publisher Copyright: Copyright {\textcopyright} 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 16th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2018 ; Conference date: 30-10-2018 Through 02-11-2018",
year = "2018",
language = "English",
series = "Principles of Knowledge Representation and Reasoning: Proceedings of the 16th International Conference, KR 2018",
publisher = "AAAI Press",
pages = "641--642",
editor = "Michael Thielscher and Francesca Toni and Frank Wolter",
booktitle = "Principles of Knowledge Representation and Reasoning",
}