Frank van Harmelen — accepted AI-conference papers

2023

A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference

Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak, Frank Van Harmelen, Annette Ten Teije

NeurIPS 2023poster

We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perform exponential-time exact inference, limiting the scalability of PNL solutions. We introduce Approximate Neurosymbolic I…

Cited by 48 Source ↗PDF ↗code ↗Scholar ↗

2022

Reinforcement Learning with Option Machines

Floris den Hengst, Vincent Francois-Lavet, Mark Hoogendoorn, Frank van Harmelen

IJCAI 2022poster

Reinforcement learning (RL) is a powerful framework for learning complex behaviors, but lacks adoption in many settings due to sample size requirements. We introduce a framework for increasing sample efficiency of RL algorithms. Our approach focuses on optimizing environment rewards with high-level…

Cited by 12 Source ↗PDF ↗Scholar ↗