Statistical Relational Artificial Intelligence
Logic, Probability, and Computation
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Article
Structure learning for relational logistic regression: an ensemble approach
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLR are first-order formulae with associated weight vectors instead of scalar weig...
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Chapter
Motivation
There are good arguments that an intelligent agent that makes decisions about how to act in a complex world needs to model its uncertainty; it cannot just act pretending that it knows what is true. An agent al...
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Chapter
Inference in Propositional Models
In order to prepare the stage for inference in relational probabilistic models, we first briefly review standard probabilistic and logical inference.
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Chapter
Learning Probabilistic and Logical Models
In order to prepare the stage for learning relational probabilistic models, we first briefly review standard probabilistic and logical learning techniques.
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Chapter
Beyond Basic Probabilistic Inference and Learning
So far, we have shown how to combine logic and probabilities for standard inference tasks such as computing marginals, MAP, and learning the structure of relational probabilistic models. In many real-world app...
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Chapter and Conference Paper
Representation, Reasoning, and Learning for a Relational Influence Diagram Applied to a Real-Time Geological Domain
Mining companies typically process all the material extracted from a mine site using processes which are extremely consumptive of energy and chemicals. Sorting the rocks containing valuable minerals from ones ...
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Chapter
Statistical and Relational AI Representations
Artificial intelligence (AI) is the study of computational agents that act intelligently [Russell and Norvig, 2010, Poole and Mackworth, 2010] and, although it has drawn on many research methodologies, AI researc...
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Chapter
Inference in Relational Probabilistic Models
In this chapter, we touch upon the problem of performing inference in relational probabilistic models. Inference in probabilistic relational models refers to computing the posterior distribution of some random...
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Chapter
Conclusions
Real agents need to deal with uncertainty and reason about individuals and relations. They need to learn how the world works before they have encountered all the individuals they need to reason about. If we ac...
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Chapter
Relational Probabilistic Representations
Probability theory can be seen as extending the propositional calculus to include uncertainty; we can ask for the probability of a proposition conditioned on a proposition. Likewise, the (first-order) predicat...
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Book
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Chapter
Representational Issues
When dealing with complex domains, there are many diverse pieces of information that should be taken into account for an informed decision, and there are diverse needs for data. This explains why there are so ...
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Chapter
Learning Probabilistic Relational Models
So far, we have assumed that we were given a relational probabilistic model, i.e., both logical structure and the parameters were assumed to be given. Usually, however, this assumption does not hold and we hav...
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Chapter and Conference Paper
Population Size Extrapolation in Relational Probabilistic Modelling
When building probabilistic relational models it is often difficult to determine what formulae or factors to include in a model. Different models make quite different predictions about how probabilities are af...
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Chapter and Conference Paper
Image Quality Assessment Using the SSIM and the Just Noticeable Difference Paradigm
The structural similarity index (SSIM) has been shown to be a superior objective image quality metric. A web-based pilot experiment was conducted with the goal of quantifying, through the use of a sample of hu...
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Article
Open AccessILP turns 20
Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infa...
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Chapter and Conference Paper
Probabilistic Relational Learning and Inductive Logic Programming at a Global Scale
Building on advances in statistical-relational AI and the Semantic Web, this talk outlined how to create knowledge, how to evaluate knowledge that has been published, and how to go beyond the sum of human know...
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Chapter and Conference Paper
Logic, Probability and Computation: Foundations and Issues of Statistical Relational AI
Over the last 25 years there has been considerable body of research into combinations of predicate logic and probability forming what has become known as (perhaps misleadingly) statistical relational artificia...
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Chapter and Conference Paper
Semantic Science: Ontologies, Data and Probabilistic Theories
This chapter overviews work on semantic science. The idea is that, using rich ontologies, both observational data and theories that make (probabilistic) predictions on data are published for the purposes of impro...
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Chapter
The Independent Choice Logic and Beyond
The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set...
