This website is dedicated to the editorial and author-support activities of the journal 'Artificial Intelligence', usually referred to as the AIJ. It does not document the publisher's activities (but see the link to Elsevier's website at the top right of this page, "AIJ@Elsevier").

FREE ACCESS TO ARTIFICIAL INTELLIGENCE JOURNAL IS RE-ESTABLISHED!!
FOR FURTHER INFORMATION PLEASE SEE http://aij.ijcai.org/free-access-to-the-ai-journal

The editorial process is organized by the Artificial Intelligence Journal Division (AIJD) of IJCAI.

All submissions should be made via the Elsevier's Editorial System (EES). Submission via email is not possible. 

- AIJ has approved a new policy regarding under what circumstances a submission to AIJ may include work previously published in a conference paper by the same author

 

The Results of the 2019 SUMMER CALL FOR FUNDING:

For this call (summer 2019) we received 34 applications. Given the budget of 120.000 Euros and the criteria for the evaluation of the applications, the sponsorship committe decided to grant the following 18 applications.

 

THE WINNERS OF THE 2019 CLASSIC AND PROMINENT PAPER AWARDS ARE:

2019 Classic AIJ Paper Award

Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
RS Sutton, D Precup, S Singh - Artificial intelligence, 1999 - Elsevier

URL: https://www.sciencedirect.com/science/article/pii/S0004370299000521

This is the seminal paper on options, a widely used framework in reinforcement learning (RL)  for representing actions at different temporal scales and levels of abstraction. The option framework has  shaped subsequent work in the area, and still defines  the terms in which the problem of representation learning of hierarchical  actions and knowledge is formulated and addressed in RL.

2019 Prominent AIJ Paper Award

The dropout learning algorithm
P Baldi, P Sadowski - Artificial intelligence, 2014 - Elsevier

URL: https://www.sciencedirect.com/science/article/pii/S0004370214000216

Dropout has become a key machine learning technique used mainly to avoid  overfitting when training large neural networks. The mathematical analysis in the paper uncovers the properties that have made dropout a cornerstone in deep learning. The article also provides connections to other areas of statistics, and to the theory of ensemble learning and spiking neurons.


Full information on the AIJ Award Page.

 

FUNDING OPPORTUNITIES for PROMOTING AI RESEARCH

Here are the Results from the Winter 2019 Call for Funding.