Java Pommerman: Partial-Observable Bomberman

• Start year: 2019
• URL: https://github.com/GAIGResearch/java-pommerman

Members involved

• Diego Perez Liebana
• Raluca Gaina
• Martin Balla
• Ercument Ilhan
• Olve Drageset
• Simon Lucas

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About

Pommerman is a complex multi-player and partially observable game where agents try to be the last standing to win. This game poses very interesting challenges to AI, such as collaboration, learning and planning. We compare two Statistical Forward Planning algorithms, Monte Carlo Tree Search (MCTS) and Rolling Horizon Evolutionary Algorithm (RHEA) in Pommerman. We provide insights on how the agents actually play the game, inspecting their behaviours to explain their performance. Results show that MCTS outperforms RHEA in several game settings, but leaving room for multiple avenues of future work: tuning these methods, improving opponent modelling, identifying trap moves and introducing of assumptions for partial observability settings.

Associated research

• Diego Perez Liebana, Raluca D. Gaina, Olve Drageset, Ercument Ilhan, Martin Balla, Simon M. Lucas, “Analysis of Statistical Forward Planning Methods in Pommerman”, in Proceedings of the Artificial intelligence and Interactive Digital Entertainment (AIIDE), 15, pp. 66-72, 2019.