Under Consideration for Publication in Theory and Practice of Logic Programming Inference and Learning in Probabilistic Logic Programs Using Weighted Boolean Formulas

Citations in the corpus

• DeepProbLog: Neural Probabilistic Logic Programming (2018, methodology): “For full details, we refer to Fierens et al. [2015].”
• ProbLog2: Probabilistic Logic Programming (2015, background): “If we add asthma(3) as evidence, the conditional probability of ?- asthma(2) is 0.”
• Hashing-Based Approximate Probabilistic Inference in Hybrid Domains (2015, methodology): “…algorithms for Bayesian networks (Chavira and Darwiche, 2008), their relational extensions (Chavira et al., 2006), factor graphs (Choi et al., 2013), probabilistic programs (Fierens et al., 2013), and probabilistic databases (Suciu et al., 2011) reduce their inference problem to a WMC computation.”
• Weighted Rules under the Stable Model Semantics (2016, unknown): “Example 2 “Markov Logic has the drawback that it cannot express (non-ground) inductive definitions” (Fierens et al. 2015) because it relies on classical models.”
• Uncertainty Aware AI ML: Why and How (2018, background): “The parameters of ProbLog programs can be learned from partial interpretations (Fierens et al. 2015), and ProbLog rules defining a target predicate can be learned from a ProbLog program specifying background knowledge (in the form of facts and/or known rules for other predicates) and ground atoms…”
• Probabilistic Logical Inference on the Web (2016, background): “A similar system is ProbLog2 [4], which has also an online version(3).”
• Probabilistic Inference in Hybrid Domains by Weighted Model Integration (2015, methodology): “…algorithms for Bayesian networks [Chavira and Darwiche, 2008], their relational extensions [Chavira et al., 2006], factor graphs [Choi et al., 2013], probabilistic programs [Fierens et al., 2013], and probabilistic databases [Suciu et al., 2011] reduce their inference problem to a WMC computation.”