72 citations · 121 across the 9 of their papers we have counts for
7 papers · 1 filter
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments
Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt
The integration of learning and reasoning is high on the research agenda in AI. Nevertheless, there is only a little attention to use existing background knowledge for reasoning ab…
Semirings for Probabilistic and Neuro-Symbolic Logic Programming
Vincent Derkinderen, Robin Manhaeve, Pedro Zuidberg Dos Martires +1
The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages…
smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation
Pietro Totis, Angelika Kimmig, Luc De Raedt
Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabil…
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve +3
Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to…
Safe Reinforcement Learning via Probabilistic Logic Shields
Wen-Chi Yang, Giuseppe Marra, Gavin Rens +1
Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to…
Flexible constrained sampling with guarantees for pattern mining
Vladimir Dzyuba, Matthijs van Leeuwen, Luc De Raedt
Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns…