A New Challenge: Approaching Tetris Link with AI
  Decades of research have been invested in making computer programs for
playing games such as Chess and Go. This paper focuses on a new game, Tetris
Link, a board game that is still lacking any scientific analysis. Tetris Link
has a large branching factor, hampering a traditional heuristic planning
approach. We explore heuristic planning and two other approaches: Reinforcement
Learning, Monte Carlo tree search. We document our approach and report on their
relative performance in a tournament. Curiously, the heuristic approach is
stronger than the planning/learning approaches. However, experienced human
players easily win the majority of the matches against the heuristic planning
AIs. We, therefore, surmise that Tetris Link is more difficult than expected.
We offer our findings to the community as a challenge to improve upon.
