Logic-based Learning of Interpretable Knowledge
from Raw Data (Invited talk)
Alessandra M. Russo
Faculty of Engineering, Department of Computing, Imperial College, London, UK


                                      Abstract
                                      In this talk, I will overview the state-of-the-art of logic-based machine learning for learning different
                                      classes of logic-based programs, (e.g., non-monotonic, non-deterministic and preference-based). I will
                                      then present how the advanced features of this family of systems can help the development of innovative
                                      neuro-symbolic AI solutions that combine statistical learning for fast “low-level” perception from
                                      unstructured data, with “high-level” symbolic learning of interpretable knowledge in a range of tasks. I
                                      will show that our neuro-symbolic solutions outperform differentiable baseline systems in accuracy and
                                      data efficiency.




CILC’23: 38th Italian Conference on Computational Logic, June 21–23, 2023, Udine, Italy
$ a.russo@imperial.ac.uk (A. M. Russo)
 0000-0002-3318-8711 (A. M. Russo)
                                    © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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