BEGIN:VCALENDAR VERSION:2.0 PRODID:ILLC Website X-WR-TIMEZONE:Europe/Amsterdam BEGIN:VTIMEZONE TZID:Europe/Amsterdam X-LIC-LOCATION:Europe/Amsterdam BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19700329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19701025T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:/NewsandEvents/Archives/2022/newsitem/13438/1- August-2022-The-9th-Workshop-on-Probabilistic-Logi c-Programming-PLP-2022-Haifa-Israel DTSTAMP:20220220T222959 SUMMARY:The 9th Workshop on Probabilistic Logic Pr ogramming (PLP 2022), Haifa, Israel DTSTART;VALUE=DATE:20220801 DTEND;VALUE=DATE:20220801 LOCATION:Haifa, Israel DESCRIPTION:Probabilistic logic programming (PLP) approaches have received much attention in this ce ntury. They address the need to reason about relat ional domains under uncertainty arising in a varie ty of application domains, such as bioinformatics, the semantic web, robotics, and many more. Develo pments in PLP include new languages that combine l ogic programming with probability theory as well a s algorithms that operate over programs in these f ormalisms. By promoting probabilities as explicit programming constructs, inference, parameter estim ation and learning algorithms can be run over prog rams that represent highly structured probability spaces. Partly due to logic programming's strong t heoretical underpinnings, PLP is fast becoming a v ery well founded area of probabilistic programming . This workshop provides a forum for the exchange of ideas, presentation of results and preliminary work in all areas related to probabilistic logic programming. While PLP has already contributed a n umber of formalisms, systems and well understood a nd established results in: parameter estimation, t abling, marginal probabilities and Bayesian learni ng, many questions remain open in this exciting, e xpanding field in the intersection of AI, machine learning and statistics. The workshop encompasses all aspects of combining logic, algorithms, progra mming and probability. It aims to bring together r esearchers in all aspects of probabilistic logic p rogramming, including theoretical work, system imp lementations and applications. Interactions betwee n theoretical and applied minded researchers are e ncouraged. A mixture of papers are sought includi ng: new results, work in progress as well as techn ical summaries of recent substantial contributions . Papers presenting new results should be 6-15 pag es in length. Work in progress and technical summa ries can be shorter (2-5 pages). Contributions sho uld be prepared in the 1-column CEURART style. Sub missions will be managed via EasyChair. At least o ne author of each accepted paper will be required to attend the workshop to present the contribution . X-ALT-DESC;FMTTYPE=text/html:
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.
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.