Efficient Detection of Exchangeable Factors in Factor Graphs


  • Malte Luttermann German Research Center for Artificial Intelligence
  • Johann Machemer University of Lübeck
  • Marcel Gehrke University of Lübeck




To allow for tractable probabilistic inference with respect to domain sizes, lifted probabilistic inference exploits symmetries in probabilistic graphical models. However, checking whether two factors encode equivalent semantics and hence are exchangeable is computationally expensive. In this paper, we efficiently solve the problem of detecting exchangeable factors in a factor graph. In particular, we introduce the detection of exchangeable factors (DEFT) algorithm, which allows us to drastically reduce the computational effort for checking whether two factors are exchangeable in practice. While previous approaches iterate all O(n!) permutations of a factor's argument list in the worst case (where n is the number of arguments of the factor), we prove that DEFT efficiently identifies restrictions to drastically reduce the number of permutations and validate the efficiency of DEFT in our empirical evaluation.




How to Cite

Luttermann, M., Machemer, J., & Gehrke, M. (2024). Efficient Detection of Exchangeable Factors in Factor Graphs. The International FLAIRS Conference Proceedings, 37(1). https://doi.org/10.32473/flairs.37.1.135518



Special Track: Uncertain Reasoning