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Logic, Probability and Computation: Foundations and Issues of Statistical Relational AI

David Poole ( University of British Columbia )

Over the last 25 years there has been a considerable body of research into combinations of predicate logic and probability forming what has become known as statistical relational artificial intelligence (StaR-AI). Statistical relational artificial intelligence studies representing, reasoning and learning in uncertain and noisy domains described in terms of individuals and relations among individuals. We can model uncertainty about properties of individuals, relations among individuals, identity, and existence of individuals. I overview the foundations of the area, some research problems, proposed solutions, outstanding issues, and some misconceptions that have arisen. I discuss representations, semantics, inference, learning and applications, and provide references to the literature.  This is intended to be an overview of foundations, rather than a survey of research results.

Speaker bio

David Poole is a Professor of Computer Science at the University of British Columbia. He is known for his work on combining logic and probability, assumption-based reasoning, diagnosis, relational probabilistic models, algorithms for probabilistic inference, representations and algorithms for automated decision making, probabilistic reasoning with ontologies and semantic science. He is a co-author of two AI textbooks: "Artificial Intelligence: Foundations of Computational Agents" (Cambridge University Press, 2010), and "Computational Intelligence: A Logical Approach" (Oxford University Press, 1998), and was co-chair of AAAI-10 and UAI-94. He is the chair of the Association for Uncertainty in Artificial Intelligence, is a Fellow of the Association for the Advancement Artificial Intelligence (AAAI) and the winner of the Canadian AI Association (CAIAC) 2013 Lifetime Achievement Award. This academic year he is a Leverhulme Trust visting professor at the University of Oxford.

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