Probabilistic (logic) programming concepts

By Luc De Raedt, Angelika Kimmig. 2015

232 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

DeepProbLog: Neural Probabilistic Logic Programming (2018, methodology): “Annotated disjunctions do not change the expressivity of ProbLog, as they can alternatively be modeled through independent facts and logical rules; we refer to De Raedt and Kimmig [6] for technical details.”
Neuro-Symbolic = Neural + Logical + Probabilistic (2019, background): “, 2016; Getoor and Taskar, 2007] and probabilistic logic programming [De Raedt and Kimmig, 2015].”
Uncertainty Aware AI ML: Why and How (2018, background): “ProbLog (De Raedt, Kim-mig, and Toivonen 2007; Fierens et al. 2015) 1 belongs to a family of probabilistic logic programming (PLP) languages (De Raedt and Kimmig 2015) following Sato’s distribution semantics (Sato 1995).”
Probabilistic Logical Inference on the Web (2016, background): “In this way we hope to reach out to a wider audience and increase the user base of PLP.”
Probabilistic logic programs: Unifying program trace and possible world semantics (2017, background): “However, this distinction only affects the set F of probabilistic facts, which contains several independent copies of a fact if the PLP language does not use memoization, and a single one in case the language does use memoization.”
ProbLog2: Probabilistic Logic Programming (2015, methodology): “From a modeling perspective, a ProbLog program has two parts: (1) a probabilistic part that defines a probability distribution over truth values of a subset of the program’s atoms, and (2) a logical part that derives truth values of remaining atoms using a reasoning mechanism similar to Prolog.”
Logical Induction (2016, unknown): “67”
Solving Probability Problems in Natural Language (2017, background): “Probabilistic programming [De Raedt and Kimmig, 2015] is an increasingly popular programming paradigm in which declarative programming languages are extended with probabilistic primitives and inference mechanisms.”