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ProbLog: A Probabilistic Prolog and its Application in Link Discovery
TLDR
The key contribution of this paper is the introduction of an effective solver for computing success probabilities, which essentially combines SLD-resolution with methods for computing the probability of Boolean formulae.
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DeepProbLog: Neural Probabilistic Logic Programming
TLDR
This work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.
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A short introduction to probabilistic soft logic
TLDR
This paper provides an overview of the PSL language and its techniques for inference and weight learning.
On the implementation of the probabilistic logic programming language ProbLog
TLDR
Algorithms that allow the efficient execution of queries in ProbLog are introduced, their implementation on top of the YAP-Prolog system is discussed, and their performance in the context of large networks of biological entities is evaluated.
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Probabilistic (logic) programming concepts
TLDR
A number of core programming concepts underlying the primitives used by various probabilistic languages are identified, the execution mechanisms that they require are discussed and these are used to position and survey state-of-the-art probabilism languages and their implementation.
View on Springer
Parameter Learning in Probabilistic Databases: A Least Squares Approach
TLDR
This work introduces the problem of learning the parameters of the probabilistic database ProbLog, and develops an approach that is able to learn both from queries and from proofs and even from both simultaneously, which makes it flexible and allows faster training in domains where the proofs are available.
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Parameter estimation in ProbLog from annotated queries
TLDR
This work introduces the problem of learning the parameters of the probabilistic database ProbLog by computing the unobserved probabilities attached to facts that have a low approximation error on the training examples as well as on unseen examples.
On the Efficient Execution of ProbLog Programs
TLDR
Algorithms that allow the efficient execution of queries are introduced, their implementation on top of the YAP-Prolog system is discussed, and their performance in the context of large networks of biological entities is evaluated.
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ProbLog2: Probabilistic Logic Programming
TLDR
ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog, is presented, which offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system's components.
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The magic of logical inference in probabilistic programming
TLDR
This work introduces distributional clauses, a variation and extension of Sato's distribution semantics, and contributes a novel approximate inference method that integrates forward reasoning with importance sampling, a well-known technique for probabilistic inference.
View on Cambridge Press
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