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Probabili stic logic programming (PLP) approaches have recei ved much attention in this century. They address t he need to reason about relational domains under u ncertainty arising in a variety of application dom ains, such as bioinformatics, the semantic web, ro botics, and many more. Developments in PLP include new languages that combine logic programming with probability theory as well as algorithms that ope rate over programs in these formalisms. By promoti ng probabilities as explicit programming construct s, inference, parameter estimation and learning al gorithms can be run over programs that represent h ighly structured probability spaces. Partly due to logic programming's strong theoretical underpinni ngs, PLP is fast becoming a very well founded area of probabilistic programming.

\n\nThis wo rkshop provides a forum for the exchange of ideas, presentation of results and preliminary work in a ll areas related to probabilistic logic programmin g. While PLP has already contributed a number of f ormalisms, systems and well understood and establi shed results in: parameter estimation, tabling, ma rginal probabilities and Bayesian learning, many q uestions remain open in this exciting, expanding f ield in the intersection of AI, machine learning a nd statistics. The workshop encompasses all aspect s of combining logic, algorithms, programming and probability. It aims to bring together researchers in all aspects of probabilistic logic programming , including theoretical work, system implementatio ns and applications. Interactions between theoreti cal and applied minded researchers are encouraged.

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URL:http://stoics.org.uk/~plp2022/
CONTACT:Roberta Calegari at roberta.calegari at un
ibo.it
CONTACT:Luke Dickens at l.dickens at ucl.ac.uk
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A mixture of papers are soug ht including: new results, work in progress as wel l as technical summaries of recent substantial con tributions. Papers presenting new results should b e 6-15 pages in length. Work in progress and techn ical summaries can be shorter (2-5 pages). Co ntributions should be prepared in the 1-column CEU RART style. Submissions will be managed via EasyCh air. At least one author of each accepted paper wi ll be required to attend the workshop to present t he contribution.