  In this paper, we study AI approaches to successfully play a 2-4 players,
full information, Bomberman variant published on the CodinGame platform. We
compare the behavior of three search algorithms: Monte Carlo Tree Search,
Rolling Horizon Evolution, and Beam Search. We present various enhancements
leading to improve the agents' strength that concern search, opponent
prediction, game state evaluation, and game engine encoding. Our top agent
variant is based on a Beam Search with low-level bit-based state representation
and evaluation function heavy relying on pruning unpromising states based on
simulation-based estimation of survival. It reached the top one position among
the 2,300 AI agents submitted on the CodinGame arena.
