Dynamische Probabilistische Relationale Modelle

Projektleiter: Prof. Dr. Ralf Möller

Wissenschaftlicher Mitarbeiter: Marcel Gehrke

In praktischen Anwendung ist es für Agenten wichtig, über eine große Menge von Objekten sinnvoll schlussfolgern zu können (und nicht nur über ein Objekt oder wenige). Dabei sollte erreicht werden, dass nicht jedes Objekt einzeln betrachtet werden muss, sondern für Gruppen von Objekten mit gleichen Eigenschaften jeweils Stellvertreter betrachtet werden können. In den Anwendung ist Information über einzelne Objekte mit Unsicherheit belegt, so dass Schlussfolgerungen über Unsicherheiten unterstützt werden müssen, und auch das zugrundeliegende Modell berücksichtigt Unsicherheiten.

Information über einzelne Objekte werden vom Agenten über der Zeit akquiriert, so dass auch zeitliches Schließen eine große Bedeutung in pratischen Anwendungen hat. Wenn nun aber Evidenz über einzelne Objekte über der Zeit eintrifft, so wird es immer weniger möglich, für Gruppen von Objekten effizient über Stellvertreter zu schließen, so dass Schlussfolgerungsprozesse zur Berechnung von optimalen Handlungen immer langsamer werden. In der Praxis sollte also über der Zeit wieder von "Einzeleindrücken" abstrahiert werden, ohne zu große Fehler für Einzelobjekte zu erzeugen.

Die Veröffentlichung löst das zeitliche Informationsabstraktionsproblem zum ersten Male im Rahmen von temporalen (dynamischen) probabilistischen relationalen Modellen (DPRMs) und stellt damit einen wichtigen Schritt zur stabilen Datenverarbeitung in einem Agenten dar, so dass Handlungen auch mit fortschreitend eintreffender Information über verschiedene Objekte effizient berechnet werden können und vom Agenten trotzdem viele Einzelobjekte "im Blick behalten werden können"

Im Rahmen von COPICOH haben wir DPRMs im Bereich Medizin und Gesundheit angwendet. Im DFG-Exzellencluster UWA modellieren wir semantische Repräsentationen von Texten mit DPRMs.

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.

LDJT für Textverstehen

Im DFG-Exzellenzcluster "Understanding Written Artefacts" setzen wir LDJT ein, um semantisches Repräsentationslernen zu studieren.

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

2024

  • Malte Luttermann, Johann Machemer, Marcel Gehrke: Efficient Detection of Exchangeable Factors in Factor Graphs
    in: Proceedings of the Thirty-Seventh International FLAIRS Conference (FLAIRS-24), 2024, Florida Online Journals
    BibTeX
  • Malte Luttermann, Mattis Hartwig, Tanya Braun, Ralf Möller, Marcel Gehrke: Lifted Causal Inference in Relational Domains
    in: Proceedings of the Third Conference on Causal Learning and Reasoning (CLeaR-24), 2024, PMLR, Vol.236, p.827-842
    Website BibTeX
  • Malte Luttermann, Tanya Braun, Ralf Möller, Marcel Gehrke: Colour Passing Revisited: Lifted Model Construction with Commutative Factors
    in: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), 2024, AAAI Press, Vol.38, p.20500-20507
    DOI BibTeX
  • Magnus Bender, Tanya Braun, Ralf Möller, Marcel Gehrke: ReFrESH – Relation-preserving Feedback-reliant Enhancement of Subjective Content Descriptions
    in: 18th IEEE International Conference on Semantic Computing, (ICSC 2024), February 5-7, 2024, IEEE, p.17-24
    Website BibTeX
  • Magnus Bender, Tanya Braun, Ralf Möller, Marcel Gehrke: Unsupervised Estimation of Subjective Content Descriptions in an Information System
    in: International Journal of Semantic Computing, 2024, Vol.18, (1)
    DOI BibTeX
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2023

  • Marcel Gehrke: Dissertation Abstract: Taming Exact Inference in Temporal Probabilistic Relational Models
    in: KI-Künstliche Intelligenz, 2023, p.1-6
    DOI Website BibTeX
  • Magnus Bender, Tanya Braun, Ralf Möller, Marcel Gehrke: LESS is More: LEan Computing for Selective Summaries
    in: KI 2023: Advances in Artificial Intelligence, 2023, Springer Nature Switzerland, p.1-14
    Website BibTeX
  • Magnus Bender, Kira Schwandt, Ralf Möller, Marcel Gehrke: FrESH – Feedback-reliant Enhancement of Subjective Content Descriptions by Humans
    in: Proceedings of the Workshop on Humanities-Centred Artificial Intelligence (CHAI 2023), , CEUR Workshop Proceedings, p.15-24
    Website BibTeX
  • Nadja Redzuan, Marcel Gehrke, Ralf Möller, Tanya Braun: On Domain-specific Topic Modelling Using the Case of a Humanities Journal
    in: Proceedings of the Workshop on Humanities-Centred Artificial Intelligence (CHAI 2023), , CEUR Workshop Proceedings
    BibTeX
  • Malte Luttermann, Ralf Möller, Marcel Gehrke: Lifting Factor Graphs with Some Unknown Factors
    in: Proceedings of the Seventeenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-23), 2023, Springer, Vol.14294, p.337-347
    DOI BibTeX
  • Florian Andreas Marwitz, Ralf Möller, Marcel Gehrke: PETS: Predicting Efficiently using Temporal Symmetries in Temporal PGMs
    in: Proceedings of the Seventeenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-23), 2023, Springer
    Website BibTeX
  • Magnus Bender, Tanya Braun, Ralf Möller, Marcel Gehrke: Unsupervised Estimation of Subjective Content Descriptions
    in: 17th IEEE International Conference on Semantic Computing, (ICSC 2023), February 1-3, 2023, IEEE
    Website BibTeX
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2022

  • Marcel Gehrke, Ralf Möller, Tanya Braun: Who did it? Identifying the Most Likely Origins of Events
    in: Proceedings of the 11th International Conference on Probabilistic Graphical Models (PGM 2022), 2022, p.217-228
    Website BibTeX
  • Tanya Braun, Marcel Gehrke: Explainable and Explorable Decision Support
    in: Proceedings of the 27th International Conference on Conceptual Structures (ICCS 2022), Münster, Germany, September 12-15, 2022, 2022
    DOI Website BibTeX
  • Tanya Braun, Marcel Gehrke, Florian Lau, Ralf Möller: Lifting in Multi-agent Systems under Uncertainty
    in: 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), Eindhoven, Netherlands, August 1-5, 2022, 2022
    Website BibTeX
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2021

  • Magnus Bender, Tanya Braun, Marcel Gehrke, Felix Kuhr, Ralf Möller, Simon Schiff: Identifying and Translating Subjective Content Descriptions Among Texts
    in: International Journal of Semantic Computing, 2021, Vol.15, (4), p.461-485
    DOI BibTeX
  • Marcel Gehrke: Taming Exact Inference in Temporal Probabilistic Relational Models
    University of Lübeck, 2021, PhD thesis
    BibTeX
  • Marcel Gehrke: On the Complexity and Completeness of the Lifted Dynamic Junction Tree Algorithm
    in: 10th International Workshop on Statistical Relational AI at the 1st International Joint Conference on Learning and Reasoning, 2021
    Website BibTeX
  • Tanya Braun, Marcel Gehrke, Tom Hanika, Nathalie Hernandez (Eds.): ICCS-21 Proceedings of the 26th International Conference on Conceptual Structures
    Springer, 2021
    DOI BibTeX
  • Nils Finke, Tanya Braun, Marcel Gehrke, Ralf Möller: Concept Drift Detection in Dynamic Probabilistic Relational Models
    in: The International FLAIRS Conference Proceedings, 2021, Vol.34
    DOI BibTeX
  • Nils Finke, Tanya Braun, Marcel Gehrke, Ralf Möller: Dynamic Domain Sizes in Temporal Probabilistic Relational Models
    in: The International FLAIRS Conference Proceedings, 2021, Vol.34
    DOI BibTeX
  • Magnus Bender, Tanya Braun, Marcel Gehrke, Felix Kuhr, Ralf Möller, Simon Schiff: Identifying Subjective Content Descriptions Among Texts
    in: 15th IEEE International Conference on Semantic Computing, (ICSC 2021), Laguna Hills, CA, USA, January 27-29, 2021, IEEE, p.9-16
    DOI BibTeX
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2020

  • Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten, Ralf Möller: Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping
    in: Proceedings of the 10th International Conference on Probabilistic Graphical Models, 2020, 23-25 Sep, Manfred Jaeger, Thomas Dyhre Nielsen volu (Ed.), PMLR, Proceedings of Machine Learning Research, p.197-208
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Simon Polovina: Restricting the Maximum Number of Actions for Decision Support under Uncertainty
    in: ICCS-20 Proceedings of the 25th International Conference on Conceptual Structures, 2020
    DOI BibTeX
  • Marcel Gehrke, Ralf Möller, Tanya Braun: Taming Reasoning in Temporal Probabilistic Relational Models
    in: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), 2020, p.2592 - 2599
    DOI 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
    DOI 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, Lecture Notes in Computer Science, Vol.11919, 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: Graph-Based Representation and Reasoning - 24th International Conference on Conceptual Structures, (ICCS 2019), Marburg, Germany, July 1-4,, 2019, Springer, Lecture Notes in Computer Science, Vol.11530, 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, Lecture Notes in Computer Science, Vol.11489, 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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