Search Results for author: Luc De Raedt

Found 44 papers, 9 papers with code

Mapping probability word problems to executable representations

no code implementations EMNLP 2021 Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc De Raedt, Walter Daelemans

While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically.

Contextualised Word Representations Math +2

CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments

no code implementations5 Mar 2024 Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt

In CLEVR-POC, knowledge in the form of logical constraints needs to be leveraged to generate plausible answers to questions about a hidden object in a given partial scene.

Language Modelling Large Language Model +2

Semirings for Probabilistic and Neuro-Symbolic Logic Programming

no code implementations21 Feb 2024 Vincent Derkinderen, Robin Manhaeve, Pedro Zuidberg Dos Martires, Luc De Raedt

The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic.

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

no code implementations24 Aug 2023 Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt

Large Language Models (LLMs) have demonstrated impressive planning abilities due to their vast "world knowledge".

World Knowledge

Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach

no code implementations17 Apr 2023 Rishi Hazra, Luc De Raedt

By resorting to a neuro-symbolic approach, DERRL combines relational representations and constraints from symbolic planning with deep learning to extract interpretable policies.


smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation

no code implementations3 Apr 2023 Pietro Totis, Angelika Kimmig, Luc De Raedt

The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs.

Neural Probabilistic Logic Programming in Discrete-Continuous Domains

no code implementations8 Mar 2023 Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt

Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learning under uncertainty.

Probabilistic Programming

Declarative Probabilistic Logic Programming in Discrete-Continuous Domains

no code implementations21 Feb 2023 Pedro Zuidberg Dos Martires, Luc De Raedt, Angelika Kimmig

The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called distribution semantics.

Probabilistic Programming

Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains

no code implementations7 Nov 2022 Gavin Rens, Wen-Chi Yang, Jean-François Raskin, Luc De Raedt

The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert.

Relational Reasoning

Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation

no code implementations8 Feb 2022 Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt

Combinatorial optimisation problems are ubiquitous in artificial intelligence.

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

1 code implementation26 Jan 2022 Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt

Three types of independencies are important to represent and exploit for scalable inference in hybrid models: conditional independencies elegantly modeled in Bayesian networks, context-specific independencies naturally represented by logical rules, and independencies amongst attributes of related objects in relational models succinctly expressed by combining rules.

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

no code implementations5 Oct 2021 Pietro Totis, Angelika Kimmig, Luc De Raedt

Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning, and a novel approach to probabilistic argumentation problems based on such framework.

Probabilistic Programming

A Table-Based Representation for Probabilistic Logic: Preliminary Results

no code implementations5 Oct 2021 Simon Vandevelde, Victor Verreet, Luc De Raedt, Joost Vennekens

We present Probabilistic Decision Model and Notation (pDMN), a probabilistic extension of Decision Model and Notation (DMN).

Shape Inference and Grammar Induction for Example-based Procedural Generation

1 code implementation21 Sep 2021 Gillis Hermans, Thomas Winters, Luc De Raedt

Designers increasingly rely on procedural generation for automatic generation of content in various industries.

From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey

no code implementations25 Aug 2021 Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, Luc De Raedt

This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.

Logical Reasoning

Learning Mixed-Integer Linear Programs from Contextual Examples

no code implementations15 Jul 2021 Mohit Kumar, Samuel Kolb, Luc De Raedt, Stefano Teso

In this paper, we study the problem of acquiring MILPs from contextual examples, a novel and realistic setting in which examples capture solutions and non-solutions within a specific context.


DeepStochLog: Neural Stochastic Logic Programming

1 code implementation23 Jun 2021 Thomas Winters, Giuseppe Marra, Robin Manhaeve, Luc De Raedt

Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard.

Lifted Model Checking for Relational MDPs

no code implementations22 Jun 2021 Wen-Chi Yang, Jean-François Raskin, Luc De Raedt

We present pCTL-REBEL, a lifted model checking approach for verifying pCTL properties of relational MDPs.

Model-based Reinforcement Learning reinforcement-learning +1

Automating Data Science: Prospects and Challenges

no code implementations12 May 2021 Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.

AutoML BIG-bench Machine Learning

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

no code implementations7 Apr 2021 Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, Jesse Davis

Our key conclusion is that teams would score more goals if they shot more often from outside the penalty box in a small number of team-specific locations.

counterfactual Decision Making

Discovering Textual Structures: Generative Grammar Induction using Template Trees

1 code implementation9 Sep 2020 Thomas Winters, Luc De Raedt

In this paper, we introduce a novel grammar induction algorithm for learning interpretable grammars for generative purposes, called Gitta.

Text Generation

Human-Machine Collaboration for Democratizing Data Science

no code implementations23 Apr 2020 Clément Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt

Everybody wants to analyse their data, but only few posses the data science expertise to to this.


From Statistical Relational to Neuro-Symbolic Artificial Intelligence

no code implementations18 Mar 2020 Luc De Raedt, Sebastijan Dumančić, Robin Manhaeve, Giuseppe Marra

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.

Logical Reasoning Position

Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring

no code implementations24 Feb 2020 Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson, Amy Loutfi, Luc De Raedt

To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations.

Object Relational Reasoning

Neural Probabilistic Logic Programming in DeepProbLog

no code implementations NeurIPS 2018 Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.

Program induction

Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations

no code implementations WS 2019 Ozan Arkan Can, Pedro Zuidberg Dos Martires, Andreas Persson, Julian Gaal, Amy Loutfi, Luc De Raedt, Deniz Yuret, Alessandro Saffiotti

Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human.

Scalable Rule Learning in Probabilistic Knowledge Bases

1 code implementation AKBC 2019 Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van Den Broeck, Luc De Raedt

In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding.

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

no code implementations2 Jul 2018 Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt

In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain encompassing additionally continuous random variables.

Automating Personnel Rostering by Learning Constraints Using Tensors

no code implementations29 May 2018 Mohit Kumar, Stefano Teso, Luc De Raedt

Many problems in operations research require that constraints be specified in the model.


DeepProbLog: Neural Probabilistic Logic Programming

4 code implementations NeurIPS 2018 Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt

We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.

Program induction

Sketched Answer Set Programming

1 code implementation21 May 2017 Sergey Paramonov, Christian Bessiere, Anton Dries, Luc De Raedt

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

Context-based Object Viewpoint Estimation: A 2D Relational Approach

no code implementations21 Apr 2017 Jose Oramas, Luc De Raedt, Tinne Tuytelaars

To estimate the viewpoint (or pose) of an object, people have mostly looked at object intrinsic features, such as shape or appearance.

Action Recognition Object +4

Flexible constrained sampling with guarantees for pattern mining

1 code implementation28 Oct 2016 Vladimir Dzyuba, Matthijs van Leeuwen, Luc De Raedt

Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy.

Semiring Programming: A Declarative Framework for Generalized Sum Product Problems

no code implementations21 Sep 2016 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.

Bayesian Inference BIG-bench Machine Learning

The Inductive Constraint Programming Loop

no code implementations12 Oct 2015 Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis

Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems.

BIG-bench Machine Learning Scheduling

Graph Invariant Kernels

no code implementations Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015) 2015 Francesco Orsini, Paolo Frasconi, Luc De Raedt

Vertices of the subgraphs are then compared by a kernel that combines the similarity of their labels and the similarity of their structural role, using a suitable vertex invariant.

Graph Classification

Lazy Explanation-Based Approximation for Probabilistic Logic Programming

no code implementations10 Jul 2015 Joris Renkens, Angelika Kimmig, Luc De Raedt

We introduce a lazy approach to the explanation-based approximation of probabilistic logic programs.

Exploring the efficacy of molecular fragments of different complexity in computational SAR modeling

no code implementations13 Jan 2015 Albrecht Zimmermann, Björn Bringmann, Luc De Raedt

An important first step in computational SAR modeling is to transform the compounds into a representation that can be processed by predictive modeling techniques.

Inference and learning in probabilistic logic programs using weighted Boolean formulas

no code implementations25 Apr 2013 Daan Fierens, Guy Van Den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, Luc De Raedt

This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs.

kLog: A Language for Logical and Relational Learning with Kernels

no code implementations17 May 2012 Paolo Frasconi, Fabrizio Costa, Luc De Raedt, Kurt De Grave

The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification.

General Classification Inductive logic programming +1

Bayesian Logic Programs

no code implementations23 Nov 2001 Kristian Kersting, Luc De Raedt

Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations.

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