An ICLP workshop
18-24 September 2020, Rende, Italy
Probabilistic logic programming (PLP) approaches have received much attention in this century. They address the need to reason about relational domains under uncertainty arising in a variety of application domains, such as bioinformatics, the semantic web, robotics, and many more. Developments in PLP include new languages that combine logic programming with probability theory as well as algorithms that operate over programs in these formalisms.
The workshop encompasses all aspects of combining logic, algorithms, programming and probability.
PLP is part of a wider current interest in probabilistic programming. By promoting probabilities as explicit programming constructs, inference, parameter estimation and learning algorithms can be run over programs which represent highly structured probability spaces. Due to logic programming's strong theoretical underpinnings, PLP is one of the more disciplined areas of probabilistic programming. It builds upon and benefits from the large body of existing work in logic programming, both in semantics and implementation, but also presents new challenges to the field. PLP reasoning often requires the evaluation of large number of possible states before any answers can be produced thus breaking the sequential search model of traditional logic programs.
While PLP has already contributed a number of formalisms, systems and well understood and established results in: parameter estimation, tabling, marginal probabilities and Bayesian learning, many questions remain open in this exciting, expanding field in the intersection of AI, machine learning and statistics.
This workshop aims to bring together researchers in all aspects of probabilistic logic programming, including theoretical work, system implementations and applications. Interactions between theoretical and applied minded researchers are encouraged. The presence of this workshop at ICLP is intended to encourage collaboration with researchers from the field of logic programming,
This workshop provides a forum for the exchange of ideas, presentation of results and preliminary work, in the following areas
The above list should be interpreted broadly and is by no means exhaustive.
COVID-19: Due to the ongoing pandemic, the workshop will be held online.
The Joy of Probabilistic Answer Set Programming
Probabilistic Answer Set Programming (PASP) combines rules, facts, and independent probabilistic facts. By adopting an appropriate semantics, we can use PASP as a very powerful and flexible programming paradigm. In this talk we will first discuss this programming paradigm through examples; then we will examine the complexity and expressivity of PASP; finally, we will look at PASP's inference problem.
|Notification to authors:|
|Camera ready version due:||Sep, 10th 2020|
|Workshop date:||September 18-24, 2020|
(the deadline for all dates is intended Anywhere on Earth (UTC-12))
Submissions will be managed via EasyChair: https://easychair.org/conferences/?conf=plp2020.
Contributions should be prepared in the LNCS style. A mixture of papers are sought including: new results, work in progress as well as technical summaries of recent substantial contributions. Papers presenting new results should be 6-15 pages in length. Work in progress and technical summaries can be shorter (2-5 pages). The workshop proceedings will clearly indicate the type of each paper.
At least one author of each accepted paper will be required to attend the workshop to present the contribution.