Sisteme Hibride de Invatare Automata si Aplicatii

TL;DR

This paper introduces hybrid AI systems combining deep neural networks with neuro-symbolic models like Logic Tensor Networks, enhancing interpretability and reasoning in classification and regression tasks.

Contribution

It presents a novel hybrid approach integrating deep learning and symbolic AI, with detailed comparison and analysis of their performance and interpretability.

Findings

Both methods achieve similar precision in predictions.

Logic Tensor Networks enable interactive accuracy and reasoning.

Discussion of overfitting mitigation and scalability issues.

Abstract

In this paper, a deep neural network approach and a neuro-symbolic one are proposed for classification and regression. The neuro-symbolic predictive models based on Logic Tensor Networks are capable of discriminating and in the same time of explaining the characterization of bad connections, called alerts or attacks, and of normal connections. The proposed hybrid systems incorporate both the ability of deep neural networks to improve on their own through experience and the interpretability of the results provided by symbolic artificial intelligence approach. To justify the need for shifting towards hybrid systems, explanation, implementation, and comparison of the dense neural network and the neuro-symbolic network is performed in detail. For the comparison to be relevant, the same datasets were used in training and the metrics resulted have been compared. A review of the resulted metrics shows that while both methods have similar precision in their predictive models, with Logic Tensor Networks being also possible to have interactive accuracy and deductive reasoning over data. Other advantages and disadvantages such as overfitting mitigation and scalability issues are also further discussed.