KR2023Proceedings of the 20th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 2-8, 2023.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-02-7

Sponsored by
Published by

Copyright © 2023 International Joint Conferences on Artificial Intelligence Organization

Grounding LTLf Specifications in Image Sequences

  1. Elena Umili(Sapienza University of Rome)
  2. Roberto Capobianco(Sapienza University of Rome, Sony AI)
  3. Giuseppe De Giacomo(University of Oxford, Sapienza University of Rome)


  1. Grounding representations in the physical world
  2. Integrating symbolic and sub-symbolic approaches
  3. Neural-symbolic learning
  4. Applications of KR in computer vision


A critical challenge in neuro-symbolic (NeSy) approaches is to handle the symbol grounding problem without direct supervision. That is mapping high-dimensional raw data into an interpretation over a finite set of abstract concepts with a known meaning, without using labels. In this work, we ground symbols into sequences of images by exploiting symbolic logical knowledge in the form of Linear Temporal Logic over finite traces (LTLf) formulas, and sequence-level labels expressing if a sequence of images is compliant or not with the given formula. Our approach is based on translating the LTLf formula into an equivalent deterministic finite automaton (DFA) and interpreting the latter in fuzzy logic. Experiments show that our system outperforms recurrent neural networks in sequence classification and can reach high image classification accuracy without being trained with any single-image label.