92 citations · 252 across the 18 of their papers we have counts for
13 papers · 1 filter
Neurosymbolic Action Model Learning under Partial Observability
Adem Kikaj, Lennert De Smet, Giuseppe Marra +1
AI planning studies how an agent can reach a goal by executing a sequence of actions. To plan correctly, the agent needs an action model describing when each action can be executed…
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…
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains
Pedro Zuidberg Dos Martires, Luc De Raedt, Angelika Kimmig
Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of prob…
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey
Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve +1
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.…
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…
Semiring Programming: A Declarative Framework for Generalized Sum Product Problems
Vaishak Belle, Luc De Raedt
To solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic reasoning, machine learning and mathematical programming. Although it is widely accepted…
The Inductive Constraint Programming Loop
Christian Bessiere, Luc De Raedt, Tias Guns +7
Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously…
kLog: A Language for Logical and Relational Learning with Kernels
Paolo Frasconi, Fabrizio Costa, Luc De Raedt +1
We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a lang…
Logical Hidden Markov Models
L. De Raedt, K. Kersting, T. Raiko
Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat character…