Cognitive Module Networks for Grounded Reasoning

The necessity for neural-symbolic integration becomes evident as more complex problems like visual question answering are beginning to be addressed, which go beyond such limited-domain tasks as classification. Many existing state-of-the-art models are designed for a particular task or even benchmark, while general-purpose approaches are rarely applied to a wide variety of tasks demonstrating high performance. We propose a hybrid neural-symbolic framework, which tightly integrates the knowledge representation and symbolic reasoning mechanisms of the OpenCog cognitive architecture and one of the contemporary deep learning libraries, PyTorch, and show how to implement some existing particular models in our general framework.