SRAI 2014 - The AAAI 2014 Workshop on Statistical Relational AI

The main purpose of the Statistical Relational AI (StarAI) workshop is to bring together researchers and practitioners from two fields: logical (or relational) AI and probabilistic (or statistical) AI. These fields 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. Our long term goal is to change this by achieving a synergy between logical and statistical AI, and this workshop will serve as a stepping stone towards realizing this big picture view on AI. Previous workshops on this topic were held in conjunction with AAAI-2010, UAI-2012, and AAAI-2013, and were among the most popular workshops at the conferences.
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.
Topics
The focus of the workshop will be on general-purpose representation, reasoning and learning tools for StarAI as well as practical applications. Specifically, the workshop will encourage active participation from researchers in the following communities: satisfiability (SAT), constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP), graphical models and probabilistic reasoning (UAI), statistical learning (NIPS and ICML), graph mining (KDD and ECML PKDD) and probabilistic databases (VLDB and SIGMOD). It will also actively involve researchers from more applied communities, such as natural language processing (ACL and EMNLP), information retrieval (SIGIR, WWW and WSDM), vision (CVPR and ICCV), semantic web (ICSW and ESWC) and robotics (RSS and ICRA).
Format
We intend the Statistical Relational AI workshop to be a one day session with around 50 attendees, a number of paper presentations and poster spotlights, a poster session, and invited speakers. Confirmed invited speakers include Henry Kautz (University of Rochester, USA), and Vibhav Gogate (University of Texas, Dallas, USA).