* External authors




Extending Real Logic with Aggregate Functions

Samy Badreddine

Michael Spranger

* External authors

IJCLR-2021, NeSy Workshop



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

Logic Tensor Networks

Artificial Intelligence, 2022
Samy Badreddine, Artur d'Avila Garcez*, Luciano Serafini*, Michael Spranger

Attempts at combining logic and neural networks into neurosymbolic approaches have been on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists deep learning, which typically uses a sub-symbolic distributed representation, to learn and reason a…

Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation

NeurIPS, 2021
Ryuji Imamura*, Takuma Seno, Kenta Kawamoto, Michael Spranger

When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environ…

RecipeBowl: A Cooking Recommender for Ingredients and Recipes using Set Transformer

IEEE Access, 2021
Michael Spranger, Kana Maruyama

Countless possibilities of recipe combinations challenge us to determine which additional ingredient goes well with others. In this work, we propose RecipeBowl which is a cooking recommendation system that takes a set of ingredients and cooking tags as input and suggests pos…


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.