Recent Announcements
Thanks
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. We wanted to thank David Smith for his generous financial contributions to the organization, as well as Fetch Technologies, UCLA, CMU, 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.
DOIs
JAIR is in the process of registering DOIs for future (and eventually existing) articles published in the journal. We wish to acknowledge the generous support of IJCAI, Inc. for an endowment which supports our ongoing annual costs to register DOIs.
2011 Annual Report
We are pleased to present the annual report on the status of JAIR. The report covers the activities of the journal during 2010 and the first half of 2011, as well as historical data on previous years.
Best Paper Prize Announcement
The IJCAI-JAIR Best Paper Prize is awarded to an outstanding paper published in JAIR in the preceding five calendar years. The 2011 Prize Committee selected the following paper on mechanism design that shows that classical results in mechanism design theory often fail when naively combined with suboptimal algorithms, resulting in a mechanism that is no longer necessarily truthful. The authors also propose a way of addressing this problem, by giving agents a chance to improve the output of the underlying algorithm. This work has been highly cited and is considered one of the starting points of what has become a major research area in Artificial Intelligence and algorithmic game theory. The prize and accompanying cheque will be presented at IJCAI-11 in Barcelona.
2011 Prize: N. Nisan and A. Ronen (2007) "Computationally Feasible VCG Mechanisms" Volume 29, 19-47.
An Honorable Mention is given to the following paper which presents one of the first attempts to integrate learning techniques into modern planners. The techniques it introduced for solving the complex class of relational Markov Decision Processes had important impact on the use of learning in classical planning.
2011 Honorable Mention: A. Fern, S. Yoon and R. Givan (2006) "Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes", Volume 25, 75-118.
