An ICLP workshop
21st, September 2019, Las Cruces, New Mexico, USA
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.
The workshop will take place at Las Cruces, New Mexico, USA.
DeepLPMLN: A neural probabilistic logic programming language
This talk will introduce DeepLPMLN, which extends probabilistic answer set programming language LPMLN by embracing neural networks via the notion of neural atoms borrowed from DeepProbLog. The formalism allows for seamless coordination of the perception and the reasoning tasks, as well as joint parameter learning between probabilistic logic programs and neural networks. The gradients from the rules do not stop at updating the weights of the rules but could backpropagate further into neural networks, thereby enabling neural networks to learn not only from implicit correlations from the data but also from explicit complex semantic constraints conveniently expressed by answer set programs.
Efficient inference in discrete and continuous domains for PLP languages under the Distribution Semantics
In Probabilistic Logic Programming, a large number of languages have been independently proposed. Many of these however follow a common approach, the distribution semantics (Sato 1995). Since PLP systems generally must solve a large number of inference problems in order to perform learning, they rely critically on the support of efficient inference systems. The talk will provide an overview of the most recent and scalable techniques for exact and approximate reasoning on PLP programs under the distribution semantics, in the presence of discrete or continuous random variables.
Registration open through ICLP registration: https://shopcart.nmsu.edu/shop/icpl2019
|Papers due:|| |
|Notification to authors:|| |
|Camera ready version due:|| |
|Workshop date:||21, September 2019|
(the deadline for all dates is 23:59 BST)
At least one author of each accepted paper will be required to attend the workshop to present the contribution.