AIJul 28, 2018

Towards Explainable Inference about Object Motion using Qualitative Reasoning

arXiv:1807.10935v12 citations
Originality Incremental advance
AI Analysis

This addresses the need for transparent inference in domains like forensics, but it is incremental as it builds on qualitative reasoning methods for a specific physical process.

The paper tackled the problem of making explainable inferences about object motion, which is challenging in fields like forensics, by developing a qualitative theory for rigid object motion in 3D space and a reasoning method to infer causes of movement from rest, without relying on black-box physics simulations.

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 forensics science. Most of the work in the literature relies on physics simulation to draw such infer- ences. The simulation requires a precise model of the under- lying 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 advan- tage in making transparent inferences with ambiguous infor- mation, which makes it suitable for this task. However, there has been no suitable qualitative theory proposed for object motion in three-dimensional space. In this paper, 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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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