Artificial intelligence approaches to build ticket to ride maps
Ticket To Ride, game design, artificial intelligence
Fun, as a game trait, is challenging to evaluate. Previous research explores game arc and game refinement to improve the quality of games. Fun, for some players, is having an even chance to win while executing their strategy. To explore this, we build boards for the game Ticket to Ride while optimizing for a given win rate between four AI agents. These agents execute popular strategies human players use: one-step thinking, long route exploitation, route focus, and destination hungry strategies. We create the underlying graph of a map by connecting several planar bipartite graphs. To build the map, we use a multiple phase design, with each phase implementing several simplified Monte Carlo Tree Search components. Within a phase, the components communicate with each other passively. The experiments show that the proposed approach results in improvements over randomly generated graphs and maps.
Smith, I., & Anton, C. (2022). Artificial Intelligence Approaches to Build Ticket to Ride Maps. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12844-12851. https://doi.org/10.1609/aaai.v36i11.21564
Proceedings from the Thirty-Sixth AAAI Conference on Artificial Intelligence held virtually on February 22-March 1, 2022.
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