Scalable Neural-Probabilistic Answer Set Programming

Understand

The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI.

• Deep Probabilistic Programming Languages (DPPLs) have been developed for probabilistic logic programming to be carried out via the probability estimations of deep neural networks.
• However, recent SOTA DPPL approaches allow only for limited conditional probabilistic queries and do not offer the power of true joint probability estimation.
• In our work, we propose an easy integration of tractable probabilistic inference within a DPPL.