=Paper=
{{Paper
|id=Vol-2998/invited1
|storemode=property
|title=Learning and Reasoning with Logic Tensor Networks: The Framework and an Application (Abstract of Invited Talk)
|pdfUrl=https://ceur-ws.org/Vol-2998/invited1.pdf
|volume=Vol-2998
|authors=Luciano Serafini
|dblpUrl=https://dblp.org/rec/conf/icann/Serafini21
}}
==Learning and Reasoning with Logic Tensor Networks: The Framework and an Application (Abstract of Invited Talk)==
<pdf width="1500px">https://ceur-ws.org/Vol-2998/invited1.pdf</pdf>
<pre>
Learning and Reasoning with Logic Tensor Networks:
The Framework and an Application (Abstract of
Invited Talk)
Luciano Serafini1
1
    Fondazione Bruno Kessler, Trento, Italy



Logic Tensor Networks (LTN) is a theoretical framework and an experimental platform that
integrates learning based on tensor neural networks with reasoning using first-order many-
valued/fuzzy logic. LTN supports a wide range of reasoning and learning tasks with logical
knowledge and data using rich symbolic knowledge representation in first-order logic (FOL) to
be combined with efficient data-driven machine learning based on the manipulation of real-
valued vectors. In practice, FOL reasoning including function symbols is approximated through
the usual iterative deepening of clause depth. Given data available in the form of real-valued
vectors, logical soft and hard constraints and relations which apply to certain subsets of the
vectors can be specified compactly in FOL. All the different tasks can be represented in LTN as
a form of approximated satisfiability, reasoning can help improve learning, and learning from
new data may revise the constraints thus modifying reasoning. We apply LTNs to Semantic
Image Interpretation (SII) in order to solve the following tasks: (i) the classification of an image’s
bounding boxes and (ii) the detection of the relevant part-of relations between objects. The
results shows that the usage of background knowledge improves the performance of pure
machine learning data driven methods.




3rd International Workshop on Data meets Applied Ontologies in Explainable Artificial Intelligence (DAO-XAI 2021)
$ serafini@fbk.eu (L. Serafini)
                                       © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073
                                       CEUR Workshop Proceedings (CEUR-WS.org)

</pre>