CALL FOR PAPERS: Ninth Workshop on Statistical Relational Artificial Intelligence (StaRAI), February 2020, New York.
Co-located with AAAI 2020 Workshop Webpage: www.starai.org/2020 The purpose of the Statistical Relational AI (StarAI) workshop is to bring together researchers and practitioners from three fields: logical (or relational) AI/learning, probabilistic (or statistical) AI/learning and neural approaches for AI/learning with knowledge graphs and other structured data. 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 synergy between logical, statistical and neural AI. As a stepping stone towards realising this big-picture view on AI, we are organizing the Ninth International Workshop on Statistical Relational AI at AAAI 2020 in New York, February 7-12, 2020. KEY DATES: * Papers due: Nov 15, 2020 * Notification: Dec 3, 2020 * Camera-ready due: Jan 15, 2020 * Day of Workshop: February 7-8, 2020 SUBMISSIONS: Authors should submit a full paper reporting on: - novel technical contributions or work in progress (AAAI style, up to 7 pages excluding references), - a short position paper (AAAI style, up to 2 pages excluding references), - an already published work (verbatim, no page limit, citing original work) in PDF format via EasyChair. All submitted papers will be carefully peer-reviewed by multiple reviewers and low-quality or off-topic papers will be rejected. Accepted papers will be presented as a short talk or poster. Submission site: easychair.org/conferences/?conf=starai2020 TOPICS: StarAI 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 StarAI 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. We seek to invite researchers in all subfields of AI to attend the workshop and to explore together how to reach the goals imagined by the early AI pioneers. 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), knowledge representation (KR), constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP), graphical models and probabilistic reasoning (UAI), statistical learning (NeurIPS, ICML, and AISTATS), graph mining (KDD and ECML PKDD), probabilistic databases (VLDB and SIGMOD), relational embeddings and neural-symbolic integration (NeurIPS and ICLR). 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 (ISWC and ESWC) and robotics (RSS and ICRA). PRACTICAL: StarAI will be a one day workshop with short paper presentations, a poster session, and three invited speakers. ORGANIZING COMMITTEE: Sebastijan Dumančić (KU Leuven) Angelika Kimmig (Cardiff University) David Poole (UBC) Jay Pujara (USC)
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