We present a new lifted inference algorithm, C-FOVE, that not only handles counting formulas in its input, but also creates counting formulas for use in intermediate potentials.Expand

We present results on combining Inductive Logic Programming (ILP) with Bayesian networks to learn both the qualitative and quantitative components of Bayesian logic programs.Expand

We outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces.Expand

We present a new and simple BP algorithm, called counting BP, that exploits additional symmetries not reflected in the graphical structure and hence not exploitable by efficient inference techniques.Expand

In recent years, there has been a significant interest in integrating probability theory with first order logic and relational representations [see De Raedt and Kersting, 2003, for an overview].Expand

Logical hidden Markov models (LOHMMs) upgrade traditional hidden MarkOV models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters.Expand