Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

By Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole. 2016

327 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020, methodology): “Statistical relational learning (Raedt et al., 2016) represents another interesting approachs that aims to combine logical abstraction and relations with probability and statistics, as does recent work by Vergari et al. on probabilistic circuits (Vergari, Di Mauro, & Van den Broek, 2019).”
DeepProbLog: Neural Probabilistic Logic Programming (2018, methodology): “Indeed, today there exist approaches that tightly integrate logical and probabilistic reasoning with statistical learning; cf. the areas of statistical relational artiﬁcial intelligence [De Raedt et al., 2016, Getoor and Taskar, 2007] and probabilistic logic programming [De Raedt and Kimmig, 2015].”
Fitted Q-Learning for Relational Domains (2020, methodology): “Also, as demonstrated in several prior work on Statistical Realtional AI models (De Raedt et al. 2016), constructing a ﬂat feature vector from a symbolic structure to train a deep model can lead to loss of information including auto-correlation and linkage (Jensen and Neville 2002 Results: To…”
Equivariance Through Parameter-Sharing (2017, methodology): “In particular, many probabilistic inference techniques have been extended to graphical models with known symmetry groups (Raedt et al., 2016; Kersting et al., 2009; Bui et al., 2012; Niepert, 2012).”
Recurrent Relational Networks (2017, methodology): “See [Raedt et al., 2016, Besold et al., 2017] for two recent surveys.”
The Symbolic Interior Point Method (2016, background): “This is unlike the situation in statistical relational learning (Getoor and Taskar 2007; De Raedt et al. 2016) where symbolic structure is being used extensively.”
Neuro-Symbolic = Neural + Logical + Probabilistic (2019, background): “…is working towards their integration, and even aims at combining probability with logic and statistical learning; cf. the areas of statistical relational artificial intelligence [De Raedt et al., 2016; Getoor and Taskar, 2007] and probabilistic logic programming [De Raedt and Kimmig, 2015].”
RelNN: A Deep Neural Model for Relational Learning (2017, background): “…fed into traditional machine learning approaches such as logistic regression, SVMs, random forests, etc. Statistical relational AI (StarAI) (De Raedt et al. 2016) aims at developing models that work directly with relational data by capturing the interdependence among properties of the objects…”
Probabilistic logic programs: Unifying program trace and possible world semantics (2017, background): “Researchers gathered in a field that became known as statistical relational learning (SRL) [1], [2] and pursued the quest for the ultimate “universal” representation and accompanying inference and learning procedures.”
Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation (2017, background): “Other statistical and probabilistic approaches, such as lifted inference, fall in this same category, including probabilistic variations of inductive logic programming (ILP) [19], which are normally restricted to Horn clauses.”
Structure learning for relational logistic regression: an ensemble approach (2018, background): “Statistical Relational Learning models (SRL) (Getoor and Taskar 2007; Raedt et al. 2016) combine the representational power of logic with the ability of probability theory specifically, and statistical models in general to model noise and uncertainty.”
SimplE Embedding for Link Prediction in Knowledge Graphs (2018, background): “Tensor factorization approaches have proved to be an effective SRL approach for KG completion [29, 4, 39, 26].”
Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “These belong to the research direction of statistical relational learning [115].”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “There is a large body of work under the umbrella of statistical relational learning [189, 125] aiming at marrying logic and probability to build probabilistic relational models.”
Diachronic Embedding for Temporal Knowledge Graph Completion (2019, background): “StaRAI: Statistical relational AI (Raedt et al. 2016) approaches are mainly based on soft (hanf-crafted or learned) rules where the probability of a world is proportional to the number of rules that are satisfied/violated in that world and the confidence for each rule.”