AI Journal - Classic and Prominent Paper Awards 2021

The AI Journal is pleased to Announce Two Awards for papers published in the journal:

- The AIJ Prominent Paper Award
recognizes outstanding papers published not more than seven years ago in the AI Journal that are exceptional in their significance and impact.

Nominations are now solicited for papers published in AIJ between 2014 and 2020 for the 2021 Prominent Paper Award.

Here is the complete list of this year's Eligible Prominent Papers.


- The AIJ Classic Paper Award
recognizes outstanding papers published at least 15 calendar years ago in the AI Journal that are exceptional in their significance and impact.

So for this year, eligible Classic papers are those published in AIJ between 1970 and 2005.


THE NOMINATIONS FOR 2021 CLASSIC AND PROMINENT PAPER AWARDS will be open until May 14, 2021.

Please nominate using this link: https://easychair.org/conferences/?conf=aij21awards

All papers can be found online on the Elseveier webpage: https://www.sciencedirect.com/journal/artificial-intelligence/issues

Information about the format of nominations and the evaluation criteria for these two awards can be found here:

AIJ Classic Paper Award.
AIJ Prominent Paper Award.

The 2020 Classic and Prominent Paper Awards were given to the following papers:

2020 AIJ Classic Paper Award

Temporal Constraint Networks

R Dechter, I Meiri, J Pearl - Artificial intelligence, 1991

URL: https://www.sciencedirect.com/science/article/abs/pii/0004370291900066

This seminal paper introduced the temporal constraint satisfaction problem (TCSP), for quantitative temporal reasoning. The TCSP, and the polytime solvable special case of simple (non-disjunctive) temporal problems, have gained widespread use in planning & scheduling and other applications. The simple and elegant problem formulation of this paper also inspired subsequent work on temporal reasoning with temporal uncertainty, preferences, and other extensions.


2020 AIJ Prominent Paper Award

Conflict-based search for optimal multi-agent pathfinding

G Sharon, R Stern, A Felner, N Sturtevant - Artificial intelligence, 2015

URL: https://www.sciencedirect.com/science/article/pii/S0004370214001386

This paper introduced an elegant and novel formalisation of the multi-agent pathfinding problem, based on the resolution of conflicts and on searching a conflict tree. Upon its publication, this work substantially improved state-of-the art performance. In the years since, the paper has become a reference in the area, with many subsequent variations, extensions and generalisations. Today, these contributions are being successfully applied to solving a broad range of important industrial applications including aircraft towing, automated warehouses, office robots and video games.


Here is the list of All Previous Award Winners.