CfP for a Research Topic on Statistical Relational AI

[StarAI-Frontiers]

=== Apologies for cross-posting ===

STATISTICAL RELATIONAL ARTIFICIAL INTELLIGENCE

Call for Papers for a Research Topic on Frontiers in Robotics and
AIhttp://journal.frontiersin.org/researchtopic/5640/statistical-relational-artificial-intelligence

Guest-edited by
Fabrizio Riguzzi, University of Ferrara
Kristian Kersting, TU Dortmund University
Marco Lippi, University of Modena and Reggio Emilia
Sriraam Natarajan, Indiana University

AIMS AND SCOPE

Statistical Relational Artificial Intelligence (StarAI) combines
logical (or relational) AI and probabilistic (or statistical) AI.
Relational AI deals very effectively with complex domains involving
many and even a varying number of entities connected by complex
relationships, while statistical AI manages well the uncertainty that
derives from incomplete and noisy descriptions of the domains. Both
fields achieved significant successes over the last thirty years.
Relational AI laid the foundation of knowledge representation and has
significantly broadened the application domain of data mining
especially in bio- and chemo-informatics. It now represents some of
the best-known examples of scientific discovery by AI systems in the
literature. Statistical AI, in particular the use of probabilistic
graphical models, has revolutionized AI, too, by exploiting
probabilistic independencies. The independencies specified in such
models are natural, provide structure that enables efficient reasoning
and learning, and allow one to model complex domains. Many AI problems
arising in a wide variety of fields such as machine learning,
diagnosis, network communication, computational biology, computer
vision, and robotics have been elegantly encoded and solved using
probabilistic graphical models.

However, both fields evolved largely independently until about fifteen
years ago, when the potential originating from their combination
started to emerge. Statistical Relational Learning (SRL) was proposed
for exploiting relational descriptions in statistical machine learning
methods from the field of graphical models. Languages such as Markov
Logic Networks, Relational Dependency Networks, PRISM, Probabilistic
Relational Models, ProbLog allow the user to reason and learn with
models that describe complex and uncertain relationships among domain
entities.

Meanwhile, the scope of SRL was significantly advanced in StarAI to
cover all forms of reasoning and models of AI. StarAI is nowadays an
ample area encompassing many and diverse approaches. One major example
is given by neural-symbolic paradigms, that address the long-standing
problem of combining symbolic and connectionist approaches for
knowledge representation, learning and reasoning, with new impulse
coming from the area of deep learning.

The goal of this Research Topic in the Computational Intelligence
specialty section of Frontiers in Robotics and AI is to collect
articles providing a picture of the current status and trends of
StarAI. We are also organizing a summer school to be held in 2018 on
StarAI and we plan to invite selected authors of papers from the
Research Topic to give lectures in the school.

A non-exhaustive list of topics of interest for this Research Topic is:

- Representation languages
- Inference algorithms
- Lifted inference
- Learning algorithms
- Complexity analyses
- Tractable languages
- Algorithm scaling
- Theoretical frameworks
- Probabilistic Programming
- Optimization
- Neural-symbolic paradigms
- Deep neural architectures for knowledge representation and reasoning

ABOUT FRONTIERS RESEARCH TOPICS

Founded by scientists in 2007, Frontiers is a community-rooted
open-access publisher, driving innovations in peer review,
article-level metrics and research networking. The "Frontiers in"
journal series hosts 54 journals covering more than 350 academic
specialties, with a network of over 200,000 leading researchers
worldwide. Frontiers is a registered member of the Open Access
Scholarly Publishers Association
(http://www.oaspa.org/member/Frontiers) and was recognized by the
ALPSP Award for Innovation in Publishing in 2014.

The idea behind a Frontiers Research Topic is to create a
comprehensive collection of peer-reviewed articles that address a
specific theme of research, as well as a forum for discussion and
debate. Contributions can be articles describing original research,
methods, hypothesis & theory, opinions, and more. Please see the
relevant journal for a full list of accepted article types.

Frontiers will also compile an e-book, as soon as all contributing
articles are published, that can be used as educational material, be
sent to foundations that fund your research, to journalists and press
agencies, or to your professional network. E-books are free to read
and download.

Once published, your articles will be free to access for all readers,
indexed in relevant repositories, and as an author in Frontiers, you
retain the copyright to your own papers and figures.

FRONTIERS PUBLISHING FEES

Manuscripts accepted for publication are subject to publishing fees,
which vary depending on the article type. Research Topic A type
articles receive a discount on publishing fees; please see here for a
full fee table, and further relevant FAQs:
http://www.frontiersin.org/about/PublishingFees.

IMPORTANT DATES

31 March 2017: abstract submission
29 September 2017: manuscript submission

SUBMISSION INSTRUCTIONS AND GUIDE FOR AUTHORS

See http://journal.frontiersin.org/researchtopic/5640/statistical-relational-artificial-intelligence
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