3 research outputs found
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone
We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game
Collaborative agent gameplay in the Pandemic board game
While artificial intelligence has been applied to control players’
decisions in board games for over half a century, little attention
is given to games with no player competition. Pandemic is an exemplar collaborative board game where all players coordinate to
overcome challenges posed by events occurring during the game’s
progression. This paper proposes an artificial agent which controls
all players’ actions and balances chances of winning versus risk
of losing in this highly stochastic environment. The agent applies
a Rolling Horizon Evolutionary Algorithm on an abstraction of
the game-state that lowers the branching factor and simulates the
game’s stochasticity. Results show that the proposed algorithm
can find winning strategies more consistently in different games
of varying difficulty. The impact of a number of state evaluation
metrics is explored, balancing between optimistic strategies that
favor winning and pessimistic strategies that guard against losing.peer-reviewe