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.

Implementation

A prototype implementation of LDJT based on BLOG and the LVE implementation by Taghipour as well as some documentation is available:

The web pages around the implementation have been prepared by Moritz Hoffmann.

Publications

2020

  • Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten, Ralf Möller: Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping
    wird veröffentlicht in: Proceedings of Machine Learning Research, 2020
    BibTeX
  • Marcel Gehrke, Tanya Braun, Simon Polovina: Restricting the Maximum Number of Actions for Decision Support under Uncertaitny
    wird veröffentlicht in: ICCS-20 Proceedings of the 25th International Conference on Conceptual Structures, 2020
    BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Taming Reasoning in Temporal Probabilistic Relational Models
    in: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), 2020
    BibTeX
  • Stefan Lüdtke, Marcel Gehrke, Tanya Braun, Ralf Möller, Thomas Kirste: Lifted Marginal Filtering for Asymmetric Models by Clustering-based Merging
    in: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), 2020
    BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Taming Reasoning in Temporal Probabilistic Relational Models
    in: 9th International Workshop on Statistical Relational AI at the 34th AAAI Conference on Artificial Intelligence, 2020
    Website BibTeX
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2019

  • Marcel Gehrke, Tanya Braun, Ralf Möller: Efficient Multiple Query Answering in Switched Probabilistic Relational Models
    in: Proceedings of AI 2019: Advances in Artificial Intelligence, 2019, Springer, p.104-116
    DOI BibTeX
  • Marcel Gehrke, Simon Schiff, Tanya Braun, Ralf Möller: Which Patient to Treat Next? Probabilistic Stream-based Reasoning for Decision Support and Monitoring
    in: Proceedings of the ICBK 2019, 2019, IEEE, p.73-80
    DOI BibTeX
  • Mattis Hartwig, Marcel Gehrke, Ralf Möller: Approximate Query Answering in Complex Gaussian Mixture Models
    in: Proceedings of the ICBK 2019, 2019, IEEE, p.81-86
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Lifted Temporal Most Probable Explanation
    in: Proceedings of the International Conference on Conceptual Structures 2019, 2019, Springer, p.72-85
    DOI BibTeX
  • Tanya Braun, Marcel Gehrke: Inference in Statistical Relational AI
    in: Proceedings of the International Conference on Conceptual Structures 2019, 2019, Springer, p.xvii-xix
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Lifted Temporal Maximum Expected Utility
    in: Proceedings of the 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, 2019, Springer, p.380-386
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Uncertain Evidence for Probabilistic Relational Models
    in: Proceedings of the 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, 2019, Springer, p.80-93
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Relational Forward Backward Algorithm for Multiple Queries
    in: Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS-19), 2019, AAAI Press, p.464-469
    Website BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller, Alexander Waschkau, Christoph Strumann, Jost Steinhäuser: Lifted Maximum Expected Utility
    in: Artificial Intelligence in Health, 2019, Springer International Publishing, p.131-141
    DOI BibTeX
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2018

  • Marcel Gehrke, Tanya Braun, Ralf Möller: Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm
    in: Proceedings of the AI 2018: Advances in Artificial Intelligence, 2018, Springer, p.543-555
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
    in: Proceedings of the AI 2018: Advances in Artificial Intelligence, 2018, Springer, p.556-562
    DOI BibTeX
  • Simon Schiff, Marcel Gehrke, Ralf Möller: Efficient Enriching of Synthesized Relational Patient Data with Time Series Data
    in: Procedia Computer Science, 2018, Vol.141, p.531 - 538, The 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2018) / Affiliated Workshops
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
    in: Proceedings of KI 2018: Advances in Artificial Intelligence, 2018, Springer, p.38-45
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
    in: 8th International Workshop on Statistical Relational AI at the 27th International Joint Conference on Artificial Intelligence, 2018
    Website BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Answering Hindsight Queries with Lifted Dynamic Junction Trees
    in: 8th International Workshop on Statistical Relational AI at the 27th International Joint Conference on Artificial Intelligence, 2018
    Website BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller, Alexander Waschkau, Christoph Strumann, Jost Steinhäuser: Towards Lifted Maximum Expected Utility
    in: Proceedings of the First Joint Workshop on Artificial Intelligence in Health in Conjunction with the 27th IJCAI, the 23rd ECAI, the 17th AAMAS, and the 35th ICML, 2018, CEUR-WS.org, CEUR Workshop Proceedings, Vol.2142, p.93-96
    Website BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Lifted Dynamic Junction Tree Algorithm
    in: Proceedings of the International Conference on Conceptual Structures, 2018, Springer, p.55-69
    DOI BibTeX
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