Abstract
For inference in probabilistic formalisms with first-order constructs, lifted variable elimination (LVE) is one of the standard approaches for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a specific representation of a first-order knowledge base and LVE in its computations. Unfortunately, LJT induces unnecessary groundings in cases where the standard LVE algorithm, GC-FOVE, has a fully lifted run. Additionally, LJT does not handle evidence explicitly. We extend LJT (i) to identify and prevent unnecessary groundings and (ii) to effectively handle evidence in a lifted manner. Given multiple queries, e.g., in machine learning applications, our extension computes answers faster than LJT and GC-FOVE.
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Braun, T., Möller, R. (2017). Preventing Groundings and Handling Evidence in the Lifted Junction Tree Algorithm. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_7
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DOI: https://doi.org/10.1007/978-3-319-67190-1_7
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