StaRAI 2017 - Seventh Workshop on Statistical Relational Artificial Intelligence (StaRAI)

Seventh Workshop on Statistical Relational Artificial Intelligence
(StaRAI), August 15 2017, Sydney.
Co-located with UAI-17
Workshop Webpage: http://www.starai.org
The 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 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. As a stepping stone towards realizing this big picture view on AI, we are organizing the Seventh International Workshop on Statistical Relational AI at the Conference on Uncertainty in Artificial Intelligence (UAI) in Sydney, on August 15th 2017.
PRACTICAL:
StarAI will be a one day workshop with around 50 attendees, paper presentations and poster spotlights, a poster session, and three invited speakers. Authors should submit either a full paper reporting on novel technical contributions or work in progress (AAAI style, up to 6 pages excluding references), a short position paper (AAAI style, up to 2 pages excluding references), or an already published work (verbatim, no page limit, citing original work) in PDF format via EasyChair.
Key Dates:
* Papers due: June 22, 2017
* Notification: July 1, 2017
* Camera-ready due: August 1, 2017
* Day of Workshop: August 15, 2017
Submission site:
https://easychair.org/conferences/?conf=starai2017
All submitted papers will be carefully peer-reviewed by multiple reviewers and low-quality or off-topic papers will be rejected. Papers will be selected either for a short oral presentation or a poster presentation.
For more information, please see the workshop website: http://www.starai.org/
INVITED SPEAKERS (tentative):
- Lise Getoor (University of California, Santa Cruz)
- Maximilian Nickel (MIT)
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 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 (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 (ISWC and ESWC) and robotics (RSS and ICRA).
ORGANIZING COMMITTEE:
Angelika Kimmig (Cardiff University and KU Leuven)
David Poole (University of British Columbia)
Jay Pujara (University of California, Santa Cruz)
Tim Rocktaeschel (University of Oxford)