IJCAI 2020poster0 citations
Commonsense Reasoning to Guide Deep Learning for Scene Understanding (Extended Abstract)
Abstract
Our architecture uses non-monotonic logical reasoning with incomplete commonsense domain knowledge, and incremental inductive learning, to guide the construction of deep network models from a small number of training examples. Experimental results in the context of a robot reasoning about the partial occlusion of objects and the stability of object configurations in simulated images indicate an improvement in reliability and a reduction in computational effort in comparison with an architecture based just on deep networks.
Knowledge Representation and Reasoning: Non-monotonic Reasoning, Common-Sense ReasoningMachine Learning: Deep LearningMachine Learning: Online LearningRobotics: Robotics and Vision
BibTeX
@inproceedings{ijcai2020p661,
title = {Commonsense Reasoning to Guide Deep Learning for Scene Understanding (Extended Abstract)},
author = {Sridharan, Mohan and Mota, Tiago},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {4760--4764},
year = {2020},
month = {7},
note = {Sister Conferences Best Papers},
doi = {10.24963/ijcai.2020/661},
url = {https://doi.org/10.24963/ijcai.2020/661},
}