Titlebook: Statistical Relational Artificial Intelligence; Logic, Probability, Luc Raedt,Kristian Kersting,David Poole Book 2016 Springer Nature Swit

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Synthesis Lectures on Artificial Intelligence and Machine Learninghttp://image.papertrans.cn/s/image/876637.jpg

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1939-4608 oxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensio

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Relational Probabilistic Representationsd by predicate symbols), individuals, and logical variables that can be universally or existentially quantified. Relational probabilistic representations can be seen as extending the predicate calculus to include probabilities, or extending probabilistic models to include relations, individuals, and variables.

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Learning Probabilistic Relational Modelsch domain knowledge and it is often also unclear which values for the parameters to chose. A more practical approach is to learn the model from data. That is, if we have access to a set of examples, we can learn the model parameters for a fixed structure, or learn (some or) all of the logical structure of the model in addition to the parameters.

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Relational Probabilistic Representationsoned on a proposition. Likewise, the (first-order) predicate calculus can be seen as extending propositional calculus to include relations (represented by predicate symbols), individuals, and logical variables that can be universally or existentially quantified. Relational probabilistic representati

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Learning Probabilistic Relational Models Usually, however, this assumption does not hold and we have to construct the model. Doing this by hand is usually problematic as this requires too much domain knowledge and it is often also unclear which values for the parameters to chose. A more practical approach is to learn the model from data.

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Beyond Basic Probabilistic Inference and Learning of relational probabilistic models. In many real-world applications, however, the problem formulation does not fall neatly into the pure probabilistic inference case. The problem may for example have a combinatorial component, hence, taking it outside the scope of standard inference tasks.

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