b'Luc De Raedt'

research

∙ 08/24/2023

SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge

Large Language Models (LLMs) have demonstrated impressive planning abili...

0 Rishi Hazra, et al. ∙

research

∙ 04/17/2023

Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach

Despite numerous successes in Deep Reinforcement Learning (DRL), the lea...

0 Rishi Hazra, et al. ∙

research

∙ 04/03/2023

smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation

Argumentation problems are concerned with determining the acceptability ...

0 Pietro Totis, et al. ∙

research

∙ 03/08/2023

Neural Probabilistic Logic Programming in Discrete-Continuous Domains

Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic bac...

0 Lennert De Smet, et al. ∙

research

∙ 03/06/2023

Safe Reinforcement Learning via Probabilistic Logic Shields

Safe Reinforcement learning (Safe RL) aims at learning optimal policies ...

0 Wen-Chi Yang, et al. ∙

research

∙ 02/21/2023

Declarative Probabilistic Logic Programming in Discrete-Continuous Domains

Over the past three decades, the logic programming paradigm has been suc...

0 Pedro Zuidberg Dos Martires, et al. ∙

research

∙ 11/07/2022

Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains

We propose a framework for learning a fragment of probabilistic computat...

0 Gavin Rens, et al. ∙

research

∙ 02/08/2022

Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation

Combinatorial optimisation problems are ubiquitous in artificial intelli...

0 Mohit Kumar, et al. ∙

research

∙ 01/26/2022

First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs

Statistical relational AI and probabilistic logic programming have so fa...

0 Nitesh Kumar, et al. ∙

research

∙ 10/05/2021

SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation

We introduce SMProbLog, a generalization of the probabilistic logic prog...

0 Pietro Totis, et al. ∙

research

∙ 10/05/2021

A Table-Based Representation for Probabilistic Logic: Preliminary Results

We present Probabilistic Decision Model and Notation (pDMN), a probabili...

0 Simon Vandevelde, et al. ∙

research

∙ 09/21/2021

Shape Inference and Grammar Induction for Example-based Procedural Generation

Designers increasingly rely on procedural generation for automatic gener...

0 Gillis Hermans, et al. ∙

research

∙ 08/25/2021

From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey

Neural-symbolic and statistical relational artificial intelligence both ...

0 Giuseppe Marra, et al. ∙

research

∙ 07/15/2021

Learning Mixed-Integer Linear Programs from Contextual Examples

Mixed-integer linear programs (MILPs) are widely used in artificial inte...

0 Mohit Kumar, et al. ∙

research

∙ 06/23/2021

DeepStochLog: Neural Stochastic Logic Programming

Recent advances in neural symbolic learning, such as DeepProbLog, extend...

0 Thomas Winters, et al. ∙

research

∙ 06/22/2021

Lifted Model Checking for Relational MDPs

Model checking has been developed for verifying the behaviour of systems...

0 Wen-Chi Yang, et al. ∙

research

∙ 05/12/2021

Automating Data Science: Prospects and Challenges

Given the complexity of typical data science projects and the associated...

84 Tijl De Bie, et al. ∙

research

∙ 04/07/2021

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

Analysis of the popular expected goals (xG) metric in soccer has determi...

13 Maaike Van Roy, et al. ∙

research

∙ 09/09/2020

Discovering Textual Structures: Generative Grammar Induction using Template Trees

Natural language generation provides designers with methods for automati...

1 Thomas Winters, et al. ∙

research

∙ 04/23/2020

Human-Machine Collaboration for Democratizing Data Science

Everybody wants to analyse their data, but only few posses the data scie...

22 Clément Gautrais, et al. ∙

research

∙ 03/18/2020

From Statistical Relational to Neuro-Symbolic Artificial Intelligence

Neuro-symbolic and statistical relational artificial intelligence both i...

15 Luc De Raedt, et al. ∙

research

∙ 02/24/2020

Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring

Robotic agents should be able to learn from sub-symbolic sensor data, an...

39 Pedro Zuidberg Dos Martires, et al. ∙

research

∙ 04/30/2019

Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations

Human-robot interaction often occurs in the form of instructions given f...

0 Ozan Arkan Can, et al. ∙

research

∙ 02/26/2019

Semantic Relational Object Tracking

This paper addresses the topic of semantic world modeling by conjoining ...

0 Andreas Persson, et al. ∙

research

∙ 07/02/2018

Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming

In probabilistic reasoning, the traditionally discrete domain has been e...

0 Pedro Zuidberg Dos Martires, et al. ∙

research

∙ 05/29/2018

Automating Personnel Rostering by Learning Constraints Using Tensors

Many problems in operations research require that constraints be specifi...

0 Mohit Kumar, et al. ∙

research

∙ 05/28/2018

DeepProbLog: Neural Probabilistic Logic Programming

We introduce DeepProbLog, a probabilistic logic programming language tha...

0 Robin Manhaeve, et al. ∙

research

∙ 05/21/2017

Sketched Answer Set Programming

Answer Set Programming (ASP) is a powerful modeling formalism for combin...

0 Sergey Paramonov, et al. ∙

research

∙ 10/28/2016

Flexible constrained sampling with guarantees for pattern mining

Pattern sampling has been proposed as a potential solution to the infamo...

0 Vladimir Dzyuba, et al. ∙

research

∙ 09/21/2016

Semiring Programming: A Framework for Search, Inference and Learning

To solve hard problems, AI relies on a variety of disciplines such as lo...

0 Vaishak Belle, et al. ∙

research

∙ 10/12/2015

The Inductive Constraint Programming Loop

Constraint programming is used for a variety of real-world optimisation ...

0 Christian Bessiere, et al. ∙

research

∙ 04/25/2013

Inference and learning in probabilistic logic programs using weighted Boolean formulas

Probabilistic logic programs are logic programs in which some of the fac...

0 Daan Fierens, et al. ∙

research

∙ 05/17/2012

kLog: A Language for Logical and Relational Learning with Kernels

We introduce kLog, a novel approach to statistical relational learning. ...

0 Paolo Frasconi, et al. ∙

research

∙ 02/14/2012

Inference in Probabilistic Logic Programs using Weighted CNF's

Probabilistic logic programs are logic programs in which some of the fac...

0 Daan Fierens, et al. ∙

Luc De Raedt

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