Our goal is to develop general-purpose techniques for probabilistic reasoning and learning in structured spaces. These spaces are characterized by complex logical constraints on what constitutes a possible world. We propose a tractable formalism, called probabilistic sentential decision diagrams, and show it effectively learns structured probability distributions in two applications: product configuration and preference learning.

@inproceedings{ChoiKRR15,
  author    = {Choi, Arthur and Van den Broeck, Guy and Darwiche, Adnan},
  title     = {Probability Distributions over Structured Spaces},
  booktitle = {Proceedings of the AAAI Spring Symposium on KRR},
  year      = {2015},
  url = {http://starai.cs.ucla.edu/papers/ChoiKRR15.pdf},
  keywords   = {workshop}
}