Statistical Relational AI
Papers from the 2014 AAAI Workshop
Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole, Workshop Organizers
Technical Report WS-14-13
Softcover version of the technical report: $35.00 softcover
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The fields of logical (or relational) AI and probabilistic (or statistical) AI share many key features and often solve similar problems and tasks. Until recently, however, research in them has progressed independently with little or no interaction. The fields often use different terminology for the same concepts and, as a result, keeping-up and understanding the results in the other field is cumbersome, thus slowing down research. This workshop's long term goal is to change this by achieving a synergy between logical and statistical AI.
Statistical relational AI is currently provoking a lot of new research and has tremendous theoretical and practical implications. Theoretically, combining logic and probability in a unified representation and building general-purpose reasoning tools for it has been the dream of AI, dating back to the late 1980s. Practically, successful statistical relational AI tools will enable new applications in several large, complex real-world domains including those involving big data, social networks, natural language processing, bioinformatics, the web, robotics and computer vision. Such domains are often characterized by rich relational structure and large amounts of uncertainty. Logic helps to effectively handle the former while probability helps her effectively manage the latter.