Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation

  • Pedro Zuidberg Dos Martires Katholieke Universiteit Leuven
  • Anton Dries Katholieke Universiteit Leuven
  • Luc De Raedt Katholieke Universiteit Leuven

Abstract

Weighted model counting has recently been extended to weighted model integration, which can be used to solve hybrid probabilistic reasoning problems. Such problems involve both discrete and continuous probability distributions. We show how standard knowledge compilation techniques (to SDDs and d-DNNFs) apply to weighted model integration, and use it in two novel solvers, one exact and one approximate solver. Furthermore, we extend the class of employable weight functions to actual probability density functions instead of mere polynomial weight functions.

Published
2019-07-17
Section
AAAI Technical Track: Reasoning under Uncertainty