1,721,075 research outputs found
OLIVAW: Mastering Othello without Human Knowledge, nor a Penny
No full textWe introduce OLIVAW, an AI Othello player adopting the design principles of the famous AlphaGo programs. The main motivation behind OLIVAW was to attain exceptional competence in a non-trivial board game at a tiny fraction of the cost of its illustrious predecessors. In this paper, we show how the AlphaGo Zero's paradigm can be successfully applied to the popular game of Othello using only commodity hardware and free cloud services. While being simpler than Chess or Go, Othello maintains a considerable search space and difficulty in evaluating board positions. To achieve this result, OLIVAW implements some improvements inspired by recent works to accelerate the standard AlphaGo Zero learning process. The main modification implies doubling the positions collected per game during the training phase, by including also positions not played but largely explored by the agent. We tested the strength of OLIVAW in three different ways: by pitting it against Edax, the strongest open-source Othello engine, by playing anonymous games on the web platform OthelloQuest, and finally in two in-person matches against top-notch human players: a national champion and a former world champion
Fast distributed algorithms for brooks-vizing colorings
No full textSpecial issue for the best papers of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 98)
Improved Distributed Algorithms for Coloring and Network Decomposition Problems
No full textACM Danny Lewin Award 199
Randomized distributed edge coloring via an extension of the Chernoff-Hoeffding bounds
No full textSIAM Journal on Computing262350-368SMJC
Near-Optimal, Distributed Edge Coloring via the Nibble Method
No full textA special issue
for the best papers of ESA 95, the 3rd European Symposium on Algorithm
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