An ICLP workshop
19th September 2020, Rende, Italy
|16:20 - 16:35||Opening|
|16:35 - 17:20||Invited Talk. Fabio Cozman: The Joy of Probabilistic Answer Set Programming|
|17:20 - 17:40||Damiano Azzolini, Fabrizio Riguzzi and Evelina Lamma: An Analysis of Gibbs Sampling for Probabilistic Logic Programs|
|17:40 - 18:00||Nitesh Kumar, Ondřej Kuželka and Luc De Raedt: Learning Distributional Programs for Relational Autocompletion|
|18:00 - 18:15||Break|
|18:15 - 18:35||David Tuckey, Krysia Broda and Alessandra Russo: Towards Structure Learning under the Credal Semantics|
|18:35 - 18:55||Felix Weitkämper: Proportional dependencies and asymptotics of probabilistic representations (Work in Progress)|
|18:55 - 19:40||Invited Talk. Sriraam Natarajan: Human Allied Learning of Symbolic Deep Models|
|19:40 - 19:45||Closing|
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.
Human Allied Learning of Symbolic Deep Models
Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce for learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. Next, I will present the recent progress that allows for more reasonable human interaction where the human input is taken as "advice" and the learning algorithm combines this advice with data. Finally, I will discuss about the potential of "closing the loop" where an agent figures out what it knows and solicits information about what it does not know. This is an important direction to realize the true goal of human allied AI.
Short biography: Dr. Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas and a RBDSCAII Distinguished Faculty Fellow at IIT Madras. He was previously an Associate Professor and earlier an Assistant Professor at Indiana University, Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison and had graduated with his PhD from Oregon State University. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award and the IU trustees Teaching Award from Indiana University. He is the program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He is the chief editor of Frontiers in ML and AI journal, an associate editor of MLJ, JAIR and DAMI journals and is the electronics publishing editor of JAIR.
The registration for PLP2020 is managed through the ICLP registration system. To register, visit ICLP registration page.
|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.