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Board games are a great source of entertainment for all ages, as they createa competitive and engaging environment, as well as stimulating learning andstrategic thinking. It is common for digital versions of board games, as anyother type of digital games, to offer the option to select the difficulty ofthe game. This is usually done by customizing the search parameters of the AIalgorithm. However, this approach cannot be extended to General Game Playingagents, as different games might require different parametrization for eachdifficulty level. In this paper, we present a general approach to implement anartificial intelligence opponent with difficulty levels for zero-sum games,together with a propose of a Minimax-MCTS hybrid algorithm, which combines theminimax search process with GGP aspects of MCTS. This approach was tested inour mobile application LoBoGames, an extensible board games platform, that isintended to have an broad catalog of games, with an emphasis on accessibility:the platform is friendly to visually-impaired users, and is compatible withmore than 92\% of Android devices. The tests in this work indicate that boththe hybrid Minimax-MCTS and the new difficulty adjustment system are promisingGGP approaches that could be expanded in future work.

Hybrid Minimax-MCTS and Difficulty Adjustment for General Game Playing