Download Statistical relational AI : papers presented at the Twenty

Statistical Relational AI
Papers Presented
Twenty-Eighth AAAI
on
Artificial
at
the
Conference
Intelligence
Technical Report WS-14-13
AAAI Press
Palo Alto, California
Contents
Organizers / iii
Preface / vii
Guy
Van den
Broeck,
Kristian
Kersting
Sriraam
Natarajan, David Poole
Papers
Lifting
Relational MAP-LPs
Using Cluster Signatures /
2
Udi Apsel, Kristian Kersting, Martin Mladenov
Efficient Markov Logic Inference for Natural Language Semantics / 9
Islam
Reasoning
in the
Beltagy, Raymond J. Mooney
Description Logic BEL Using Bayesian Networks /15
Ceylan, Rafael Penaloza
Ismail Ilkan
Parameter Estimation for Relational Kalman Filtering / 22
Jaesik Choi, EyalAmir, TianfangXu,
Albert J. Valocchi
Representation, Reasoning, and Learning for a Relational Influence
Diagram Applied to a Real-Time Geological Domain / 29
Matthew C. Dirks, Andrew Csinger, Andrew Bomber, David Poole
Extending PSL with Fuzzy Quantifiers /
Golnoosh Farnadi,
Stephen
H.
Bach,
35
Marie-Francine Moens, Use
Getoor, Martine De Cock
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting /
Eric Gribkoff, Guy Van den Broeck, Dan Suciu
Relational Logistic Regression: The Directed Analog of Markov Logic Networks / 41
Seyed Mehran Kazemi, David Buchman, Kristian Kersting Sriraam Natarajan, David Poole
Classification from One Class of Examples for Relational Domains / 44
Tushar
Applying Marginal
Khot,
Sriraam
Natarajan, Jude Shavlik
MAP Search to Probabilistic Conformant
Planning: Initial
Results / 51
Junkyu Lee, Rina Dechter, Radu Marinescu
Towards Adversarial Reasoning in Statistical Relational Domains / 57
Daniel Lowd, Brenton Lessley, Mino De Raj
Automated Debugging with Tractable Probabilistic Programming / 60
Aniruddh Nath, Pedro Domingos
Learning Tractable
Statistical Relational Models / 62
Aniruddh Nath, Pedro Domingos
v
38
Tractable Probabilistic
Knowledge Bases: Wikipedia and Beyond / 69
Mathias Niepert, Pedro
A
Sparse
Domingos
Parameter Learning Method for Probabilistic
Logic Programs /
76
Masaaki Nishino, Akihiro Yamamoto, Masaaki Nagata
A Deeper Empirical
Analysis of CBP Algorithm: Grounding Is the Bottleneck / 83
Poyrekar, Sriraam Natarajan, Kristian Kersting
Shrutika
Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics
Joris Renkens, Angelika Kimmig, Guy
A
Proposal
Van den
/ 86
Broeck, Luc De Raedt
for Statistical Outlier Detection in Relational Structures / 93
Fatemeh Riahi, Oliver Schulte, QingLi
WOLFE:
Strength Reduction and Approximate Programming for Probabilistic Programming / 100
Sebastian Riedel Sameer Singh Vivek Srikumar, Jim Rocktaschel,
Hierarchical
Larysa Visengeriyeva, Jan Noessner
Reasoning with Probabilistic Programrning /104
Ruttenberg, Matthew P. Wilkins, Avi Pfeffer
Brian E.
Scalable Learning for Structure in Markov Logic Networks /111
Zhengya Sun, Zhuoyu Wei, Jue Wang Hongwei Hao
Evidence-Based Clustering for Scalable Inference in Markov Logic /
118
Deepak Venugopal Vibhav Gogate
Solving Distributed Constraint Optimization
Mihaela Verman,
Philip
Problems Using Ranks / 125
Stutz, Abraham Bernstein
Efficient Probabilistic Inference for Dynamic Relational Models / 131
Jonas Vhsselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt
ProPPR: Efficient First-Order Probabilistic
Structure
Logic Programming for
Parameter Learning, and Scalable Inference / 133
Discovery,
Yang Wang Kathryn Mazaitis,
William
vi
William W. Cohen