Artificial Intelligence Approaches to Build Ticket to Ride Maps

Abstract

Fun, as a game trait, is difficult 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 four popular strategies in Ticket to Ride: one-step thinking, long route exploitation, route focus, and destination hungry strategies. We create the underlying graph of a map, we use a multiphase design, with each phase implementing several Monte Carlo Tree Search components. Within a phase, the components communicate with each other passively. We explore preferred map structures for each agent in 4-player, 3-player, and 2 player scenarios. The experiments show that the proposed approach results in improvements over randomly generated graphs and maps.

Department: Computer Science 

Faculty Mentor: Dr. Calin Anton