Call for Papers
The deadline for all submission dates is midnight, Central European Summer Time (UTC + 2).
ILP, NeSy, StarAI and AAIP will be soliciting paper submissions on relevant topics. All papers accepted by each conference/workshop will be presented at IJCLR.
There is a common submission page where the authors are invited to select a track to submit their paper to (ILP, NeSy, StarAI, AAIP). Submission guidelines, paper format, proceedings etc. will adhere to each conference/workshop’s tradition. Please follow the links below for more information on how to submit to ILP, NeSy, StarAI and AAIP.
In addition to the separate tracks for each event, IJCLR features a “General Track”, where authors are invited to submit work that is relevant to IJCLR, but it does not necessarily fall within the scope of one of ILP, NeSy, StarAI or AAIP.
Submissions to this track are possible via the submission page. Authors who wish to submit a paper to this track should select the “General Track” option.
Accepted papers will be presented at IJCLR. Papers should be up to 15 pages long, including references, in Springer LNCS format.
Submission dealine for the conference track (ILP, NeSy, AAIP, General Track):
June 30 2021July 7 2021 (extended).
- Author notification: August 2021.
StarAI has a different submission date on September 1st 2021.
Recently Published Papers Track
IJCLR invites high-quality papers relevant to the scope of the conference, which have been recently published, or accepted for publication, by a first-class conference such as AAAI, IJCAI, NeurIPS, ECML/PKDD, ICML, KDD, ICDM, etc., or journal such as MLJ, DMKD, JMLR etc.
Papers submitted to the “Recently Published Papers Track” will be accepted on the grounds of relevance and quality of the original publication venue. Accepted papers will be presented at IJCLR.
Authors are invited to follow the link to the submission page and select the “Recently Published Papers Track”. Authors should submit the abstract and the PDF file of the original submission, specifying in the abstract the original venue where the papar was accepted in addition to the acceptance date.
- Submission dealine for the Recently Published Papers Track: September 10 2021 (tentative).
- Author notification: September 20 2021.
IJCLR’s journal track, the special issue on Learning and Reasoning supported by the Machine Learning Journal (MLJ), has been accepting paper submissions since February 2020, on regular cut-off dates. The upcoming cut-off date is June 1 2021.
Submissions are solicited on all aspects of Learning and Reasoning and topics where machine learning is combined with machine reasoning, or knowledge representation.
Papers are published online by MLJ upon acceptance and authors of accepted papers will be invited to present their work at IJCLR.
Authors are invited to submit novel, high-quality work that has neither appeared in, nor is under consideration for publication by other journals or conferences.
The editorial team will be aiming for a turn-around time of 10-12 weeks for most submissions. Articles should preferably be no longer than 20 pages. Submissions exceeding this length will not be given priority during reviews and will be under review for a longer period but will still be considered for the special track.
The journal track has the following cut-off dates:
February 15 2020.
May 15 2020.
September 1 2020.
December 1 2020.
March 1 2021.
- June 1 2021.
The deadline on each of these dates is midnight AOE time.
All papers will be reviewed following standard reviewing procedures for Machine Learning.
Papers must be prepared in accordance to the Journal guidelines: http://www.springer.com/10994. A full range of general questions about submissions to Springer journals can be found here: https://www.springer.com/gp/authors-editors/journal-author.
Manuscripts must be submitted to: http://MACH.edmgr.com. An article is submitted to this special issue by choosing “S.I. LR 2020” as the article type.
Topics of interest for the Journal Track include, but are not limited to:
- Theory & foundations of logical & relational learning.
- Learning in various logical representations and formalisms, such as logic programming & answer set programming, first-order & higher-order logic, description logic & ontologies.
- Statistical Relational AI, including structure/parameter learning for probabilistic logic languages, relational probabilistic graphical models, kernel-based methods, neural-symbolic learning.
- Systems and techniques that integrate neural, statistical & symbolic learning.
- Systems and techniques addressing aspects of integrating learning, reasoning & optimization.
- Knowledge representation and reasoning in deep neural networks.
- Symbolic knowledge extraction from neural and statistical learning models.
- Neural-symbolic cognitive models.
- Techniques that foster explainability & trustworthiness of AI models, including combinations of machine learning with constraints & satisfiability, explainable AI frameworks and reasoning about the behaviour of machine learning models.
- Inductive methods for program synthesis.
- Example-driven programming.
- Combining logic and functional program induction.
- Meta-interpretative learning & predicate invention.
- Scaling-up logical & relational learning: parallel & distributed learning techniques, online learning and learning structured representations from data streams.
The Special Issue Guest Editors:
Nikos Katzouris, National Center for Scientific Research “Demokritos”, Greece Alexander Artikis, University of Piraeus, Greece Luc De Raedt, KU Leuven, Belgium Artur d’Avila Garcez, City University of London, UK Ute Schmid, University of Bamberg, Germany Sebastijan Dumančić, KU Leuven, Belgium Jay Pujara, University of Southern California, USA