Lifted Dynamic Junction Tree Algorithm

Abstract

Probabilistic models involving relational and temporal aspects need exact and efficient inference algorithms. Existing approaches are approximative, include unnecessary grounding, or do not consider the relational and temporal aspects of the models. One approach for efficient reasoning on relational static models given multiple queries is the lifted junction tree algorithm. In addition, for propositional temporal models, the interface algorithm allows for efficient reasoning. To leverage the advantages of the two algorithms for relational temporal models, we present the lifted dynamic junction tree algorithm, an exact algorithm to answer multiple queries efficiently for probabilistic relational temporal models with known domains by reusing computations for multiple queries and multiple time steps. First experiments show computational savings while appropriately accounting for relational and temporal aspects of models.
Original languageEnglish
Title of host publicationGraph-Based Representation and Reasoning
EditorsPeter Chapman, Dominik Endres, Nathalie Pernelle
Number of pages15
Volume10872
Place of PublicationCham
PublisherSpringer International Publishing
Publication date20.05.2018
Pages55-69
ISBN (Print)978-331991378-0
ISBN (Electronic)978-3-319-91379-7
DOIs
Publication statusPublished - 20.05.2018
Event23rd International Conference on Conceptual Structures,; ; United Kingdom; 20 June 2018 through 22 June 2018; Code
- Edinburgh, United Kingdom
Duration: 20.06.201822.06.2018
Conference number: 214239

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