  We 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.
