Venue

Date

Share

Extending Real Logic with Aggregate Functions

Samy Badreddine

Michael Spranger

IJCLR-2021, NeSy Workshop

2021

Abstract

Real Logic is a recently introduced first-order language where formulas have fuzzy truth values in the interval [0, 1] and semantics are defined concretely with real domains. The Logic Tensor Networks (LTN) framework has applied Real Logic to many important AI tasks through querying, learning, and reasoning. Motivated by real-life relational database applications, we study adding aggregate functions, such as averaging elements of a relation table, to Real Logic. The key contribution of this paper is the formalization of such functions within Real Logic. This extension is straightforward and fits coherently in the end-to-end differentiable language that Real Logic is. We illustrate it on FooDB, a food chemistry database, and query foods and their nutrients. The resulting framework combines strengths of descriptive statistics modeled by fuzzy predicates, FOL to write complex queries and formulas, and SQL-like expressiveness to aggregate insights from data tables.

Related Publications

Literature-based Hypothesis Generation: Predicting the evolution of scientific literature to support scientists

AI4X, 2025
Tarek R Besold, Uchenna Akujuobi, Samy Badreddine, Jihun Choi, Hatem ElShazly, Frederick Gifford, Chrysa Iliopoulou, Kana Maruyama, Kae Nagano, Pablo Sanchez Martin, Thiviyan Thanapalasingam, Alessandra Toniato, Christoph Wehner

Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year [1]. On the one hand, this poses anincreasingly pressing challenge: Effectively navigating this ever-growing body o…

Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget

CVPR, 2025
Vikash Sehwag, Xianghao Kong, Jingtao Li, Michael Spranger, Lingjuan Lyu

As scaling laws in generative AI push performance, they simultaneously concentrate the development of these models among actors with large computational resources. With a focus on text-to-image (T2I) generative models, we aim to unlock this bottleneck by demonstrating very l…

Argus: A Compact and Versatile Foundation Model for Vision

CVPR, 2025
Weiming Zhuang, Chen Chen, Zhizhong Li, Sina Sajadmanesh, Jingtao Li, Jiabo Huang, Vikash Sehwag, Vivek Sharma, Hirotaka Shinozaki, Felan Carlo Garcia, Yihao Zhan, Naohiro Adachi, Ryoji Eki, Michael Spranger, Peter Stone, Lingjuan Lyu

While existing vision and multi-modal foundation models can handle multiple computer vision tasks, they often suffer from significant limitations, including huge demand for data and computational resources during training and inconsistent performance across vision tasks at d…

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.