Non-monotonic Logical Reasoning Guiding Deep Learning for Explainable Visual Question Answering
This work addresses the problem of making AI systems more interpretable and data-efficient for critical applications like robotics and visual analysis, though it is incremental in combining existing techniques.
The paper tackles the limitations of deep learning models in explainability and data efficiency by integrating non-monotonic logical reasoning and decision tree induction with deep networks for visual question answering. The result shows improved accuracy with small training datasets, comparable performance with larger ones, and better explanatory answers, with incremental learning enhancing question-answering and planning reliability in simulated environments.
State of the art algorithms for many pattern recognition problems rely on deep network models. Training these models requires a large labeled dataset and considerable computational resources. Also, it is difficult to understand the working of these learned models, limiting their use in some critical applications. Towards addressing these limitations, our architecture 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 and reasons with previously unknown constraints governing the domain's states. We evaluated the architecture in the context of datasets of simulated and real-world images, and a simulated robot computing, executing, and providing explanatory descriptions of plans. Experimental results indicate that in comparison with an ``end to end'' architecture of deep networks, our architecture provides better accuracy on classification problems when the training dataset is small, comparable accuracy with larger datasets, and more accurate answers to explanatory questions. Furthermore, incremental acquisition of previously unknown constraints improves the ability to answer explanatory questions, and extending non-monotonic logical reasoning to support planning and diagnostics improves the reliability and efficiency of computing and executing plans on a simulated robot.