Commonsense Reasoning to Guide Deep Learning for Scene Understanding

被引：0

作者：

Sridharan, Mohan ^{[1
]}

Mota, Tiago ^{[2
]}

机构：

[1] Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England

[2] Univ Auckland, Elect & Comp Engn, Auckland, New Zealand

来源：

PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2020年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：4760 / 4764

页数：5

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