The Role of Synchronic Causal Conditions in Visual Knowledge Learning

Seng-Beng Ho; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 9-16

We propose a principled approach for the learning of causal conditions from actions and activities taking place in the physical environment through visual input. Causal conditions are the preconditions that must exist before a certain effect can ensue. We propose to consider diachronic and synchronic causal conditions separately for the learning of causal knowledge. Diachronic condition captures the "change" aspect of the causal relationship - what change must be present at a certain time to effect a subsequent change - while the synchronic condition is the "contextual" aspect - what "static" condition must be present to enable the causal relationship involved. This paper focuses on discussing the learning of synchronic causal conditions as well as proposing a principled framework for the learning of causal knowledge including the learning of extended sequences of cause-effect and the encoding of this knowledge in the form of scripts for prediction and problem solving.