AI Journal - Classic and Prominent Paper Awards

The AI Journal is pleased to announce two new 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.

- 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.

 

The 2016 Classic and Prominent Paper Awards were given to the following papers:

2016 CLASSIC PAPER AWARD

Real-time heuristic search
Richard E. Korf
Artificial intelligence 42 (2-3), pp 189-211, 1990

This is the seminal paper in real-time heuristic search over the basic state
model considered in AI, where actions have deterministic effects and
information is complete. While standard heuristic search methods are
aimed at solving problems off-line, real-time search methods are used on-line
for selecting the next action to do after some form of lookahead.
Korf's minimin and real-time A* algorithms take inspiration in the ideas underlying
search in 2-player games, while Learning Real-time A* (LRTA*), where the heuristic
values are updated dynamically during the search, was the first to capture two key
properties: avoidance of loops and convergence to the optimal solution. LRTA* remains
a key reference in the field, where a number of variants have been developed that work
under slightly different assumptions, including the Real-time dynamic programming
algorithm (RTDP), that can be regarded as generalization of LRTA* to Markov Decision Processes.

2016 PROMINENT PAPER AWARD

Monte-Carlo tree search and rapid action value estimation in computer Go
Sylvain Gelly and David Silver
Artificial Intelligence 175 (11), Pages 1856-1875, 2011

Go is an ancient Chinese board game that has long been considered one of
the great challenges in AI.
While for a number of years computer programs have managed to beat the world's
leading human players in games like checkers and chess, the high level of intuition
and evaluation required by Go made it tough for AI search methods to crack. This has
changed in recent years, where the last milestone was the recent defeat of the
legendary Go player Lee Se-dol by AlphaGo, a program developed by Google DeepMind.
The previous milestone, however, that enabled this breakthrough, was achieved a decade
ago through the use of Monte-Carlo Tree Search augmented with a number of
enhancements. This led to programs that defeated human professional players and
achieved master (dan) level in 9x9 Go.
That work was reported in 2007 in two papers: one by RĂ©mi Coulom in the
Int. Computer Games Association Journal, the other by Sylvain Gelly and David Silver
at the ICML 2007 conference. This AIJ paper is a follow-up of the latter, covering two of
the enhancements: rapid action value estimation and heuristic initialization. These extensions
led to a program that achieved master level in 19x19 Go for the first time.

Here is the list of All Previous Award Winners.