Integrating Non-monotonic Logical Reasoning and Inductive Learning With Deep Learning for Explainable Visual Question Answering - Riley, Heather; Sridharan, Mohan (Frontiers in Robotics and AI, 2019)
State of the art algorithms for many pattern recognition problems rely on deep network models. Training these models requires a large labelled dataset and considerable computational resources, which are not readily available in many domains. Also, it is difficult to understand the working of these learned models, which limits the use of such models in some critical applications. As a step towards addressing these limitations, the architecture described in this paper draws inspiration from research in cognitive systems, and integrates the principles of commonsense logical reasoning, inductive learning, and deep learning. In the context of answering explanatory questions about scenes and the underlying classification problems, the architecture uses deep networks for extracting features from images and for generating answers to queries. Between these deep networks, it embeds components for non-monotonic logical reasoning with incomplete commonsense domain knowledge, and for decision tree induction. It also incrementally learns previously unknown constraints governing the domain's states, and uses these constraints for subsequent reasoning. Experimental results show that in comparison with an ``end to end'' architecture based on deep networks, our architecture provides: (i) better accuracy on classification problems when the training dataset is small, and comparable accuracy with larger datasets; and (ii) more accurate answers to explanatory questions. Furthermore, we show that the incremental acquisition of the unknown constraints improves the ability to answer explanatory questions, and that the architecture can be adapted to address planning tasks on a robot.