KR2021Proceedings of the 18th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning

Online event. November 3-12, 2021.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-99-7

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Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization

Approximate Inference for Neural Probabilistic Logic Programming

  1. Robin Manhaeve(KU Leuven)
  2. Giuseppe Marra(KU Leuven)
  3. Luc De Raedt(KU Leuven, Örebro University)


  1. KR and machine learning, inductive logic programming, knowledge acquisition
  2. Logic programming, answer set programming


DeepProbLog is a neural-symbolic framework that integrates probabilistic logic programming and neural networks.

It is realized by providing an interface between the probabilistic logic and the neural networks.

Inference in probabilistic neural symbolic methods is hard, since it combines logical theorem proving with probabilistic inference and neural network evaluation.

In this work, we make the inference more efficient by extending an approximate inference algorithm from the field of statistical-relational AI. Instead of considering all possible proofs for a certain query, the system searches for the best proof.

However, training a DeepProbLog model using approximate inference introduces additional challenges, as the best proof is unknown at the start of training which can lead to convergence towards a local optimum.

To be able to apply DeepProbLog on larger tasks, we propose: 1) a method for approximate inference using an A*-like search, called DPLA* 2) an exploration strategy for proving in a neural-symbolic setting, and 3) a parametric heuristic to guide the proof search.

We empirically evaluate the performance and scalability of the new approach, and also compare the resulting approach to other neural-symbolic systems.

The experiments show that DPLA* achieves a speed up of up to 2-3 orders of magnitude in some cases.