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08/24/2023
SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge
Large Language Models (LLMs) have demonstrated impressive planning abili...
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04/17/2023
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach
Despite numerous successes in Deep Reinforcement Learning (DRL), the lea...
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04/03/2023
smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation
Argumentation problems are concerned with determining the acceptability ...
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03/08/2023
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic bac...
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03/06/2023
Safe Reinforcement Learning via Probabilistic Logic Shields
Safe Reinforcement learning (Safe RL) aims at learning optimal policies ...
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02/21/2023
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains
Over the past three decades, the logic programming paradigm has been suc...
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11/07/2022
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains
We propose a framework for learning a fragment of probabilistic computat...
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02/08/2022
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
Combinatorial optimisation problems are ubiquitous in artificial intelli...
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01/26/2022
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs
Statistical relational AI and probabilistic logic programming have so fa...
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10/05/2021
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation
We introduce SMProbLog, a generalization of the probabilistic logic prog...
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10/05/2021
A Table-Based Representation for Probabilistic Logic: Preliminary Results
We present Probabilistic Decision Model and Notation (pDMN), a probabili...
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09/21/2021
Shape Inference and Grammar Induction for Example-based Procedural Generation
Designers increasingly rely on procedural generation for automatic gener...
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08/25/2021
From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey
Neural-symbolic and statistical relational artificial intelligence both ...
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07/15/2021
Learning Mixed-Integer Linear Programs from Contextual Examples
Mixed-integer linear programs (MILPs) are widely used in artificial inte...
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06/23/2021
DeepStochLog: Neural Stochastic Logic Programming
Recent advances in neural symbolic learning, such as DeepProbLog, extend...
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06/22/2021
Lifted Model Checking for Relational MDPs
Model checking has been developed for verifying the behaviour of systems...
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05/12/2021
Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated...
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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...
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09/09/2020
Discovering Textual Structures: Generative Grammar Induction using Template Trees
Natural language generation provides designers with methods for automati...
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04/23/2020
Human-Machine Collaboration for Democratizing Data Science
Everybody wants to analyse their data, but only few posses the data scie...
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03/18/2020
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Neuro-symbolic and statistical relational artificial intelligence both i...
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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...
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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...
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02/26/2019
Semantic Relational Object Tracking
This paper addresses the topic of semantic world modeling by conjoining ...
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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...
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05/29/2018
Automating Personnel Rostering by Learning Constraints Using Tensors
Many problems in operations research require that constraints be specifi...
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05/28/2018
DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language tha...
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05/21/2017
Sketched Answer Set Programming
Answer Set Programming (ASP) is a powerful modeling formalism for combin...
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10/28/2016
Flexible constrained sampling with guarantees for pattern mining
Pattern sampling has been proposed as a potential solution to the infamo...
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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...
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10/12/2015
The Inductive Constraint Programming Loop
Constraint programming is used for a variety of real-world optimisation ...
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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...
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05/17/2012
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning. ...
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02/14/2012