Algorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI. Training these models requires a large labeled dataset and considerable computational resources, which are not readily available in many domains. Also, it is difficult to explore the internal representations and reasoning mechanisms of these models. As a step towards addressing the underlying knowledge representation, reasoning, and learning challenges, the architecture described in this paper draws inspiration from research in cognitive systems. As a motivating example, we consider an assistive robot trying to reduce clutter in any given scene by reasoning about the occlusion of objects and stability of object configurations in an image of the scene. In this context, our architecture incrementally learns and revises a grounding of the spatial relations between objects and uses this grounding to extract spatial information from input images. Non-monotonic logical reasoning with this information and incomplete commonsense domain knowledge is used to make decisions about stability and occlusion. For images that cannot be processed by such reasoning, regions relevant to the tasks at hand are automatically identified and used to train deep network models to make the desired decisions. Image regions used to train the deep networks are also used to incrementally acquire previously unknown state constraints that are merged with the existing knowledge for subsequent reasoning. Experimental evaluation performed using simulated and real-world images indicates that in comparison with baselines based just on deep networks, our architecture improves reliability of decision making and reduces the effort involved in training data-driven deep network models.

基于深度网络模型的算法被用于机器人和人工智能中的许多模式识别和决策任务。训练这些模型需要大型标记数据集和大量计算资源，这在许多领域都不容易获得。此外，很难探索这些模型的内部表示和推理机制。作为解决潜在知识表示、推理和学习挑战的一步，本文描述的架构从认知系统研究中汲取灵感。作为一个激励性的例子，我们考虑一个辅助机器人试图通过推理场景图像中对象的遮挡和对象配置的稳定性来减少任何给定场景中的混乱。在这种情况下，我们的架构逐步学习和修改对象之间空间关系的基础，并使用该基础从输入图像中提取空间信息。使用此信息和不完整的常识领域知识进行非单调逻辑推理，用于做出关于稳定性和遮挡的决策。对于无法通过这种推理处理的图像，与手头任务相关的区域会被自动识别并用于训练深度网络模型以做出所需的决策。用于训练深度网络的图像区域也用于增量获取先前未知的状态约束，这些约束与现有知识合并以进行后续推理。使用模拟和真实世界图像进行的实验评估表明，与仅基于深度网络的基线相比，