VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
This work addresses the challenge of enhancing deep generative models with logical reasoning for AI researchers, presenting a novel integration that is not incremental but introduces a new framework.
The paper tackles the problem of integrating neural networks with symbolic reasoning by proposing VAEL, a neuro-symbolic generative model that combines variational autoencoders with probabilistic logic programming, resulting in improved task generalization and data efficiency as supported by experiments.
We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) with the reasoning capabilities of probabilistic logic (L) programming. Besides standard latent subsymbolic variables, our model exploits a probabilistic logic program to define a further structured representation, which is used for logical reasoning. The entire process is end-to-end differentiable. Once trained, VAEL can solve new unseen generation tasks by (i) leveraging the previously acquired knowledge encoded in the neural component and (ii) exploiting new logical programs on the structured latent space. Our experiments provide support on the benefits of this neuro-symbolic integration both in terms of task generalization and data efficiency. To the best of our knowledge, this work is the first to propose a general-purpose end-to-end framework integrating probabilistic logic programming into a deep generative model.