Spain

The combination of logic and probability is very useful for modeling domains with complex and uncertain relationships among entities. Machine learning approaches based on such combinations have recently achieved important results, originating the fields of Statistical Relational Learning, Probabilistic Inductive Logic Programming and, more generally, Statistical Relational Artificial Intelligence.
The tutorial will concentrate on Probabilistic Logic Programming, a form of Probabilistic Programming that is receiving an increasing attention for its ability to combine powerful knowledge representation with Turing completeness.

This tutorial will introduce probabilistic logic programming and overview the main systems for learning models in these formalisms both in terms of parameters and of structure. The tutorial includes a significant hands-on experience with the systems ProbLog2 and cplint using the web applications https://dtai.cs.kuleuven.be/problog/ and http://cplint.eu that the attendants can access with their notebooks via wifi.


ECAI 2020

Probabilistic Inductive Logic Programming

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

technical paper

Equal Pay for Equal Competences : A Statistical  Approach to Address Equal Pay Gap

On Minimizing Diagonal Block-wise Differences for  Neural Network Compression

Deep Density-aware Count Regressor

DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction

Robot self other distinction active inference meets neural networks learning in a mirror

Minimality of Combined Qualitative Constraint Networks

SI-AGAN: Spatial Interpolation with Attentional Generative Adversarial Networks for Environment Monitoring

You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods

Maximal Correlation: An Alternative Criterion for Training Generative Networks

Towards an Explainable Argumentation-based Agent

https://d3smihljt9218e.cloudfront.net/transcript/1708/subtitle/828226c608b8dd839592570b5bb7cd43.vtt

English (automatic)

Probabilistic Inductive Logic Programming

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Equal Pay for Equal Competences : A Statistical Approach to Address Equal Pay Gap

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Probabilistic Inductive Logic Programming

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Next from ECAI 2020

Equal Pay for Equal Competences : A Statistical Approach to Address Equal Pay Gap

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Cognitive Logics: Features, Formalisms, and Challenges

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