StarAI 2020

Ninth International Workshop on Statistical Relational AI

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 a synergy between logical, statistical and neural AI. As a stepping stone towards realizing this big picture view on AI, we are organizing the Ninth International Workshop on Statistical Relational AI at the 34th AAAI Conference on Artificial Intelligence (AAAI) in New York, on February 7th 2020.



StarAI will be a one day workshop with short paper presentations, a poster session, and three invited speakers.

  • Guy Van den Broeck (UCLA) 
  • Lise Getoor (UC Santa Cruz) 
  • Yejin Choi (University of Washington & Allen Institute for AI) 


Authors should submit either 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), or 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 and poster.

Important Dates

  • Paper Submission: November 15 18, 2019
  • Notification of Acceptance: December 6, 2019
  • Camera-Ready Papers: January 15, 2020
  • Date of Workshop: February 7, 2020


Organizing Committee

For comments, queries and suggestions, please contact:
  • Sebastijan Dumancic (KU Leuven) 
  • Angelika Kimmig (Cardiff) 
  • David Poole (UBC) 
  • Jay Pujara (USC) 

Program Committee

  • Behrouz Babaki (Polytechnique Montreal)
  • Hendrik Blockeel (KU Leuven)
  • YooJung Choi (UCLA)
  • Jaesik Choi (UNIST)
  • Fabio Cozman (University of São Paulo)
  • Andrew Cropper (University of Oxford)
  • Golnoosh Farnadi (MILA)
  • Alberto García-Durán (EPFL)
  • Vibhav Gogate (University of Texas at Dallas)
  • Mehran Kazemi (Borealis AI)
  • Kristian Kersting (TU Darmstadt)
  • Ondřej Kuželka (Czech Technical University in Prague)
  • Mark Law (Imperial College)
  • Robin Manhaeve (KU Leuven)
    • Pasquale Minervini (UCL)
    • Aniruddh Nath (Google)
    • Scott Sanner (University of Toronto)
    • Vítor Santos Costa (University of Porto)
    • Oliver Schulte (Simon Fraser University)
    • Sammer Singh (University of California, Irvine)
    • Lucas Sterckx (Ghent University)
    • Stefano Teso (University of Trento)
    • Timothy Van Bremen (KU Leuven)
    • Guy Van den Broeck (UCLA)
    • Vincent Vercruyssen (KU Leuven)
    • Antonio Vergari (UCLA)
    • Pedro Zuidberg Dos Martires (KU Leuven)
    • Rodrigo de Salvo Braz (SRI International)


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) 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).

Previous Workshops

Previous StarAI workshops were held in conjunction with AAAI 2010, UAI 2012, AAAI 2013, AAAI 2014, UAI 2015, IJCAI 2016, UAI 2017 and IJCAI 2018 and were among the most popular workshops at the conferences.