Taming Reasoning in Temporal Probabilistic Relational Models.

Marcel Gehrke, Ralf Möller, Tanya Braun

Abstract

Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible. To counteract groundings over time and to keep reasoning polynomial by restoring a lifted representation, we present temporal approximate merging (TAMe), which incorporates (i) clustering for grouping submodels as well as (ii) statistical significance checks to test the fitness of the clustering outcome. In exchange for faster runtimes, TAMe introduces a bounded error that becomes negligible over time. Empirical results show that TAMe significantly improves the runtime performance of inference, while keeping errors small.
OriginalspracheEnglisch
TitelECAI
Seitenumfang8
Erscheinungsdatum2020
Seiten2592-2599
DOIs
PublikationsstatusVeröffentlicht - 2020

Strategische Forschungsbereiche und Zentren

  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
  • Querschnittsbereich: Intelligente Systeme

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