Logical Neural Network

Logical Neural Networks (LNNs) integrate the learning capabilities of neural networks with the reasoning power of symbolic logic, aiming to create more interpretable and reliable AI systems. Current research focuses on developing and applying LNN architectures, including probabilistic extensions (PLNNs), to diverse tasks such as medical diagnosis, multi-agent reinforcement learning, and visual semantic parsing. This neuro-symbolic approach addresses limitations of purely data-driven models by providing explainable predictions and improved performance in various domains, thereby enhancing trust and facilitating the deployment of AI in critical applications.