JAIR Now Available in ACM Library

JAIR and ACM are very pleased to announce that JAIR articles are now being hosted by the ACM Digital Library, in addition to the JAIR.org website. While the journal will continue to be managed and published as an independent, open access journal, this partnership will provide greater visibility for JAIR articles and their authors.

JAIR Transparent Publishing

When it was launched in 1993, JAIR was one of the very first open access scientific journals. Since then, JAIR has not only emerged as one of the top publication venues in artificial intelligence, but also inspired the creation of other, similarly successful open access journals.

Now, JAIR is taking another major step by launching its transparent publishing initiative. Starting today, JAIR publishes, on a regular basis, detailed metrics on its submission handling process, including empirical likelihoods for all evaluation outcomes and average times for reaching these outcomes. This transparent publishing approach is intended to provide useful information to prospective authors and valuable calibration to the editorial team and to reviewers.

More information about JAIR Transparent Publishing and the latest metrics are found here.

2020 IJCAI-JAIR Prize and Honourable Mention

We congratulate George Konidaris (Brown University, USA), Leslie Pack Kaelbling (MIT, USA) and Tomas Lozano-Perez (MIT, USA), who have been awarded the 2020 IJCAI-JAIR best paper prize for their article "From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning”, which appeared in Volume 61 (2018).

As noted in the award citation, this paper elegantly shows how to automatically construct abstract representations suitable for evaluating plans composed of sequences of high-level actions in a continuous, low-level environment. It follows a long tradition in AI of structuring agent control architectures around procedural abstraction, i.e., grounded abstract symbolic representation, establishing a principled link between high-level actions and abstract representations along with a theoretical foundation for constructing abstract representations with provable properties and a practical mechanism for autonomously learning abstract high-level representations.

We also congratulate Pannaga Shivaswamy (LinkedIn, USA) and Thorsten Joachims (Cornell University, USA), who have received in honourable mention for their article "Coactive Learning”, which appeared in Vo. 53 (2015). The official citation states that this paper introduces a novel learning paradigm that lies between traditional online learning, where the utilities of each action are visible to the algorithm, and bandit settings, where the utilities of only the optimal action are observed. It presents very solid theoretical results, showing upper bounds on expected regret, as well as an extensive experimental evaluation of a number of algorithms implementing the approach on real-world scenarios.

The IJCAI-JAIR Best Paper Prize is one of the most prestigious awards for a single publication in the field of AI. It has been awarded annually since 2003 to an outstanding paper published in JAIR in the last 5 calendar years, selected based on the significance of the work and the quality of the presentation.

This year’s selection committee consisted of Alessandro Cimatti (Fondazione Bruno Kessler, IT), Edith Elkind (University of Oxford, UK), Holger H. Hoos (Leiden University, NL and Univ. of British Columbia, CA; chair), Kristian Kersting (TU Darmstadt, DE), Mykel Kochenderfer (Stanford University, US), Jimmy Lee (Chinese University of Hong Kong, HK), João Marques-Silva (Institut de Recherche en Informatique de Toulouse, FR) and Josef Urban (Czech Institute of Informatics, Robotics and Cybernetics, CZ).

Special Track on Artificial Intelligence and COVID-19

JAIR invites submissions to a new special track on Artificial Intelligence and COVID-19 . This special track focuses on solutions that data science and AI provide to address challenges related to the global pandemic, and on relevant deployments and experiences in gearing AI to cope with COVID-19. Topics of interest in the context of COVID-19 include, but are not limited to: Machine Learning, Planning, Clustering, Knowledge Representation, Natural Language Processing, Image Processing, Temporal Data Analysis, Information Retrieval, Bioinformatics, Economic Utilities, and Risk Analysis. The deadline for submission has been revised and is now April 4th 2021.

IJCAI 2020 Journal Track

JAIR is happy to be a participant in the IJCAI Journal Track.  Papers that have been accepted at JAIR but have not appeared before at a conference are eligible for acceptance for this track, including a presentation at IJCAI 2020.  For more information see: https://www.ijcai20.org/journaltrack.html

2019 IJCAI-JAIR Prize Awarded

We congratulate Marijn Heule, Matti Järvisalo, Florian Lonsing, Martina Seidl and Armin Biere who have been awarded the 2019 IJCAI-JAIR prize for their paper "Clause Elimination for SAT and QSAT", which appeared in Volume 53 (2015).

As noted in the award citation, this paper describes fundamental and practical results on a range of clause elimination procedures as preprocessing and simplification techniques for SAT and QBF solvers. Since its publication, the techniques described therein have been demonstrated to have profound impact on the efficiency of state-of-the-art SAT and QBF solvers. The work is elegant and extends beautifully some well-established theoretical concepts. In addition, the paper gives new emphasis and impulse to pre- and in-processing techniques - an emphasis that resonates beyond the two key problems, SAT and QBF, covered by the authors.

Comments Welcome on our New Site

After years of behind-the-scenes work, we are happy to release our new upgraded site. We hope you like it, and comments are welcomed (especially constructive comments). Please send any comments to editors@jair.org.  We especially thank Scott Sanner, Sriraam Natarajan, Kane See and James MacGregor for their many hours of work on this project.

Please Support JAIR 

AI Access Foundation manages JAIR largely through the efforts of volunteers throughout the world. However, we occasionally have some small, but important, operating and infrastructure costs. Please support us by making a donation. All donations are appreciated, no matter how small, and they are tax dedectable.

And on this topic, we also want to thank David Smith for his financial contributions to the organization, as well as IJCAIInferLink Corporation and the University of Michigan for their ongoing support for JAIR's infrastructure. These gifts make it possible for JAIR to continue operating a freely-available journal.