基于多模态信息融合的神经符号推理引擎

Abstract

This paper introduces a novel neuro-symbolic reasoning engine designed to tackle complex problems by integrating the strengths of deep learning and symbolic reasoning. The core concept is to fuse multi-modal information – including images, text, and audio – to create a richer understanding of the problem domain. This understanding is then leveraged through a neuro-symbolic architecture that performs logical inference and knowledge representation. Specifically, the engine utilizes convolutional neural networks (CNNs) for feature extraction from image and audio data, and recurrent neural networks (RNNs) combined with graph neural networks (GNNs) for processing textual information. The fused features are then fed into a symbolic reasoning module, enabling the engine to derive conclusions based on logical rules and existing knowledge. Experimental results (simulated due to the absence of physical experimentation) demonstrate the potential of this approach for improved problem-solving accuracy compared to traditional methods relying solely on either deep learning or symbolic reasoning. The engine's architecture is designed for modularity and extensibility, allowing for the integration of new data modalities and reasoning rules. Future research directions include exploring different fusion techniques and developing more sophisticated symbolic reasoning methods. ---