Lifted Dynamic Junction Tree Algorithm

We work on probabilistic first-order formalisms where the domain objects are known. In these formalisms, the standard approach for inference with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently and to obtain a compact representation, the lifted junction tree algorithm (LJT) extends LVE. We extend the formalism and respectively LJT to handle temporal aspects. To be more precise, we combine the advantages of LJT and the interface algorithm in LDJT, which efficiently solves the inference problems filtering and prediction.

Additionally, we are interested in solving other inference problems, e.g. smoothing, and to learn relational temporal models from data.

LDJT is supported by CISCO. The work is carried out as part of Jointlab 1 within the COPICOH center for connected health.

Publications

2018

  • Marcel Gehrke, Tanya Braun, Ralf Möller, Alexander Waschkau, Christoph Strumann, Jost Steinhäuser: Towards Lifted Maximum Expected Utility
    wird veröffentlicht in: Proceedings of the Joint Workshop on Artificial Intelligence in Health in Conjunction with the 27th IJCAI, the 23rd ECAI, the 17th AAMAS, and the 35th ICML, 2018, Springer
    BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Lifted Dynamic Junction Tree Algorithm
    in: Proceedings of the International Conference on Conceptual Structures, 2018, Springer
    BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller, Alexander Waschkau, Christoph Strumann, Jost Steinhäuser: Towards Lifted Maximum Expected Utility
    IFIS, Universität zu Lübeck, 2018, Technical Report
    BibTeX
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