StarAI 2014

Format

StarAI will be a one day workshop with around 50 attendees, paper presentations and poster spotlights, a poster session, and three invited speakers:

• Luc De Raedt (KU Leuven, Belgium)
• Vibhav Gogate (UT Dallas, USA)
• Henry Kautz (University of Rochester, USA)

Submissions

Those interested in attending should submit either a technical paper (AAAI style, 6 pages without references) or a position statement (AAAI style, 2 pages maximum) 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. Papers will be selected either for a short oral presentation or a poster presentation.

Important Dates

• Paper Submission: April 17
• Notification of Acceptance: May 1
• Camera-Ready Papers: May 15
• Date of Workshop: July 27

Morning

•  8:50 a.m.: Welcome and introduction
•  9:00 a.m.: Invited talk by Luc De Raedt
  Title: Progress in probabilistic logic programming
  (abstract)
  Probabilistic logic programs combine the power of a programming language with a possible world semantics, typically based on Sato's distribution semantics and they have been studied for over twenty years. In this talk, I shall report on recent progress within this paradigm. It will concern an extension towards dealing with continuous distributions as well as coping with dynamics. This is the framework of distributional clauses that has been applied to several applications in robotics, for tracking relational worlds in which objects or their properties are occluded in real time. I shall also report on an upgrade of the traditional rule learning paradigm to a probabilistic logical setting. It provides a novel perspective on learning the structure of SRL models in that it has traditional rule learners such as FOIL as a special case. The ProbFOIL system has been applied to learn rules in a probabilistic database setting and on data from CMU's Never Ending Language Learner. Finally, I shall discuss some of the open challenges within probabilistic logic programming.
• 10:00 a.m.: Poster spotlights for papers 1 to 9
• 10:30 a.m.: Coffee break
• 11:00 a.m.: Invited talk by Vibhav Gogate
  Title: Fast, Lifted, Sampling-Based Inference in Statistical Relational Models
  (abstract)
  Scaling up inference algorithms so that they can handle problems having millions of random variables and billions of features is currently the main challenge in statistical relational learning. In this talk, I will present my group's work on scaling up sampling or simulation based approximate inference algorithms to address this challenge. The talk will be structured in two parts. In the first part, I will present our work on exact lifted approaches, namely approaches that exploit exact symmetries and show how they can benefit importance sampling, Gibbs sampling and their numerous advanced variations. In the second part, I will present approaches that take advantage of approximate symmetries by leveraging Database theory, approximate model counting techniques and clustering algorithms. I will conclude my presentation by discussing avenues for future work and open problems.
• 12:00 p.m.: Poster spotlights for papers 10 to 18
• 12:30 p.m.: Lunch break

Afternoon

• 2:00 p.m.: Invited talk by Henry Kautz
  Title: A Markov Logic Framework for Recognizing Complex Events from Multimodal Data
  (abstract)
  We present a general framework for complex event recognition that is well-suited for integrating information that varies widely in detail and granularity. Consider the scenario of an agent in an instrumented space performing a complex task while describing what he is doing in a natural manner. The system takes in a variety of information, including objects and gestures recognized by RGB-D and descriptions of events extracted from recognized and parsed speech. The system outputs a complete reconstruction of the agent's plan, explaining actions in terms of more complex activities and filling in unobserved but necessary events. We show how to use Markov Logic (a probabilistic extension of first-order logic) to create a model in which observations can be partial, noisy, and refer to future or temporally ambiguous events; complex events are composed from simpler events in a manner that exposes their structure for inference and learning; and uncertainty is handled in a sound probabilistic manner. We demonstrate the effectiveness of the approach for tracking kitchen activities in the presence of noisy and incomplete observations.
• 3:00 p.m.: Poster spotlights for papers 19 to 27
• 3:30 p.m.: Poster session (with coffee)
• 5:00 p.m.: End

1. Udi Apsel, Kristian Kersting and Martin Mladenov.
   Tightening Relational MAP-LPs via Symmetry of Clusters
2. Zhengya Sun, Zhuoyu Wei and Jue Wang.
   Scalable Learning for Structure in Markov Logic Networks
3. Brian Ruttenberg, Matthew Wilkins and Avi Pfeffer.
   Hierarchical Reasoning with Probabilistic Programming
4. Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor and Martine De Cock.
   Extending PSL with Fuzzy Quantifiers
5. William Yang Wang, Kathryn Mazaitis and William W Cohen.
   ProPPR: Efficient First-Order Probabilistic Logic Programming for Structure Discovery, Parameter Learning, and Scalable Inference
6. Joris Renkens, Angelika Kimmig, Guy Van den Broeck and Luc De Raedt.
   Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics
7. Ismail Ilkan Ceylan and Rafael Peñaloza.
   Reasoning in the Description Logic BEL using Bayesian Networks
8. Fatemeh Riahi and Oliver Schulte.
   A Proposal for Statistical Outlier Detection in Relational Structures
9. Islam Beltagy and Raymond Mooney.
   Efficient Markov Logic Inference for Natural Language Semantics
10. Seyed Mehran Kazemi, David Buchman, Kristian Kersting, Sriraam Natarajan and David Poole.
    Relational Logistic Regression: the Directed Analog of Markov Logic Networks
11. Tushar Khot, Sriraam Natarajan and Jude Shavlik.
    Classification from one class of examples for relational domains
12. Aniruddh Nath and Pedro Domingos.
    Learning Tractable Statistical Relational Models
13. Aniruddh Nath and Pedro Domingos.
    Automated Debugging with Tractable Probabilistic Programming
14. Masaaki Nishino, Akihiro Yamamoto and Masaaki Nagata.
    A Sparse Parameter Learning Algorithm for Probabilistic Logic Programs

15. Shrutika Poyrekar, Sriraam Natarajan and Kristian Kersting.
    A Deeper Empirical Analysis of CBP algorithm: Grounding is the Bottleneck
16. Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck and Luc De Raedt.
    Efficient Probabilistic Inference for Dynamic Relational Models
17. Mihaela Verman, Philip Stutz and Abraham Bernstein.
    Solving Distributed Constraint Optimization Problems using Ranks
18. Eric Gribkoff, Guy Van den Broeck and Dan Suciu.
    Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting
19. Deepak Venugopal and Vibhav Gogate.
    Evidence-based Clustering for Scalable Inference in Markov Logic
20. Jaesik Choi, Eyal Amir, Tianfang Xu and Albert Valocchi.
    Parameter Estimation for Relational Kalman Filtering
21. Matthew Dirks, Andrew Csinger, Andrew Bamber and David Poole.
    Representation, Reasoning and Inference for a Relational Open-World Influence Diagram Applied to a Real-Time Geological Domain
22. Sebastian Riedel, Sameer Singh, Vivek Srikumar, Tim Rocktaschel, Larysa Visengeriyeva and Jan Noessner.
    Wolfe: Strength Reduction and Approximate Programming for Probabilistic Programming
23. Junkyu Lee, Radu Marinescu and Rina Dechter.
    Applying Marginal MAP Search to Probabilistic Conformant Planning
24. Mathias Niepert and Pedro Domingos.
    Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond
25. Mathias Niepert and Guy Van den Broeck.
    Tractability through Exchangeability: A New Perspective on Efficient Lifted Inference
26. Tim Rocktaschel, Matko Bošnjak, Sameer Singh and Sebastian Riedel.
    Low-Dimensional Embeddings of Logic
27. Daniel Lowd, Brenton Lessley and Mino De Raj Manoharan.
    Towards Adversarial Reasoning in Statistical Relational Domains

Organizing Committee

• Guy Van den Broeck
  (UCLA, KU Leuven)
• Kristian Kersting
  (TU Dortmund, Fraunhofer IAIS)
• Sriraam Natarajan
  (Indiana University)
• David Poole
  (University of British Columbia)

Program Committee

Previous Workshops

Previous StarAI workshops were held in conjunction with AAAI 2010, UAI 2012, and AAAI 2013, and were among the most popular workshops at the conferences.

• StarAI 2013
• StarAI 2012
• StarAI 2010

Related Venues

• SRL 2012, SRL 2009, SRL 2006, SRL 2004, SRL 2003, and SRL 2000
• NIPS Workshop on Probabilistic Programming - 2008
• Dagstuhl Seminar on Probabilistic, Logical and Relational Learning - 2007
• Dagstuhl Seminar on Probabilistic, Logical and Relational Learning - 2005
• Neural-Symbolic Learning and Reasoning