publications

2024

1. NeSy (SPOTLIGHT)

   ULLER: A Unified Language for Learning and Reasoning

   Emile van Krieken, Samy Badreddine, Robin Manhaeve, and 1 more author

   In 18th International Conference on Neural-Symbolic Learning and Reasoning, 2024

   Abs arXiv

   The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth. There now are a wide variety of NeSy frameworks , each with its own specific language for expressing background knowledge and how to relate it to neural networks. This heterogeneity hinders accessibility for newcomers and makes comparing different NeSy frameworks challenging. We propose a unified language for NeSy, which we call ULLER, a Unified Language for LEarning and Reasoning. ULLER encompasses a wide variety of settings, while ensuring that knowledge described in it can be used in existing NeSy systems. ULLER has a neuro-symbolic first-order syntax for which we provide example semantics including classical, fuzzy, and probabilistic logics. We believe ULLER is a first step towards making NeSy research more accessible and comparable, paving the way for libraries that streamline training and evaluation across a multitude of semantics, knowledge bases, and NeSy systems.

2. AIR

   Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach

   Uchenna Akujuobi, Priyadarshini Kumari, Jihun Choi, and 4 more authors

   Artificial Intelligence Review, 2024

2023

1. Expert Syst. Appl.

   KitchenScale: Learning to predict ingredient quantities from recipe contexts

   Donghee Choi, Mogan Gim, Samy Badreddine, and 3 more authors

   Expert Systems with Applications, 2023
2. ICCC

   Recipe 2.0: Information Presentation for AI-Supported Culinary Idea Generation

   Angel Hwang, Samy Badreddine, Frederick Gifford, and 1 more author

   In Proceedings of the 14th International Conference on Computational Creativity (ICCC), 2023
3. NeSy (BEST PAPER)

   What’s Wrong with Gradient-based Complex Query Answering?

   Ouns El Harzli, Samy Badreddine, and Tarek R Besold

   In NeSy, 2023
4. Plos one

   Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules

   Tanoy Debnath, Samy Badreddine, Priyadarshini Kumari, and 1 more author

   Plos one, 2023

2022

1. AI Journal

   Logic tensor networks

   Samy Badreddine, Artur d’Avila Garcez, Luciano Serafini, and 1 more author

   Artificial Intelligence, 2022

   Abs Code

   Artificial Intelligence agents are required to learn from their surroundings and to reason about the knowledge that has been learned in order to make decisions. While state-of-the-art learning from data typically uses sub-symbolic distributed representations, reasoning is normally useful at a higher level of abstraction with the use of a first-order logic language for knowledge representation. As a result, attempts at combining symbolic AI and neural computation into neural-symbolic systems have been on the increase. In this paper, we present Logic Tensor Networks (LTN), a neurosymbolic formalism and computational model that supports learning and reasoning through the introduction of a many-valued, end-to-end differentiable first-order logic called Real Logic as a representation language for deep learning. We show that LTN provides a uniform language for the specification and the computation of several AI tasks such as data clustering, multi-label classification, relational learning, query answering, semi-supervised learning, regression and embedding learning. We implement and illustrate each of the above tasks with a number of simple explanatory examples using TensorFlow 2.

2021

1. NeSy

   Extending Real Logic with Aggregate Functions.

   Samy Badreddine, and Michael Spranger

   In NeSy, 2021
2. Book chapter

   Logic tensor networks: Theory and applications

   Luciano Serafini, Artur Garcez, Samy Badreddine, and 3 more authors

   In Neuro-Symbolic Artificial Intelligence: The State of the Art, 2021

2019

1. NeSy

   Injecting prior knowledge for transfer learning into reinforcement learning algorithms using logic tensor networks

   Samy Badreddine, and Michael Spranger

   arXiv preprint arXiv:1906.06576, 2019