{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"ProbLog is a probabilistic logic programming toolbox that combines logic programming with probability annotations to perform inference tasks like computing marginals given evidence and learning from interpretations.","skillfed_tags":["logic-programming","probabilistic-inference","knowledge-representation"],"use_cases":["Build probabilistic reasoning systems that combine logical rules with uncertain facts to compute outcome probabilities","Learn probabilistic logic programs from partial interpretations or labeled examples in AI/ML workflows","Model real-world systems with both deterministic rules and probabilistic components (e.g., sensor fusion, diagnosis)","Perform inference over knowledge bases stored in Prolog, CSV, or SQLite with probability annotations","Reduce probabilistic inference to weighted model counting for integration with knowledge compilation solvers"],"what_it_does":"ProbLog is a Python package for probabilistic logic programming that lets you write logic programs where facts can be annotated with probabilities. It solves inference problems by converting programs and queries into weighted Boolean formulas, then using algorithms from graphical models and knowledge compilation to compute answers. The package handles tasks like computing marginal probabilities given evidence and learning from partial interpretations.\n\nYou can represent knowledge bases as Prolog/Datalog facts, CSV files, SQLite tables, or Python functions. It's designed for situations where you need to encode both complex interactions between components and the inherent uncertainties in real-world data. The package is actively maintained, supports Python 3.8 through 3.12, and installs cleanly with minimal dependencies.","worth_installing":"Yes. ProbLog is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It fills a specific niche in probabilistic logic programming with a mature codebase (first released in 2015) and clear documentation. Install it if you need to combine logic programming with probabilistic reasoning; skip it if you're looking for general-purpose Bayesian inference or constraint solving."},"id":"problog","links":{"html":"https://skillfed.io/packages/problog","md":"https://skillfed.io/packages/problog.md","pypi":"https://pypi.org/project/problog/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-18","license_spdx":null,"license_treatment":"permissive","name":"problog","python_support":"unspecified","summary":"ProbLog2: Probabilistic Logic Programming toolbox"},"popularity":{"monthly_downloads":302512,"position":7821,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.2.10"}