Towards playing Risk with a hybrid Monte Carlo based agent - Ferrari, René G.; Assunção, Joaquim V. C. (Anais Estendidos do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames), 2022)
Over the last few decades, games have proven to be great test environments in the artificial intelligence field due to their well-defined rules and clear evaluation methods. Therefore, aiming at the advance in the artificial intelligence field, this paper proposes the development and analysis of a hybrid Monte Carlo based agent for the game Risk, a famous strategy board game. To do so, the proposed agent is going to face a heuristic agent based on an already tested agent. The expectation is to identify the pros, cons, and efficiency of using Monte Carlo in games like Risk.