Thus, at the beginning of each iteration, a determinization is sampled from the root information set and The selection step is changed because the probability distribution of moves is not uniformĪnd a move may be available only in fewer states of the same information set. The outgoing arcs from an opponent’s node represent the union of all moves available in every state within that information set, because the player cannot know the moves that are really available to the opponent. ISMCTS builds asymmetric search trees of information sets containing game states that are indistinguishable from the player’s point of view.Įach node represents an information set from the root player’s point of view and arcs correspond to moves played by the corresponding player. MCTS to decisions with imperfect information about the game state and performed actions. introduced Information Set Monte Carlo Tree Search
The experiment involving human players suggests that ISMCTS might be more challenging than traditional strategies. The best known and most studied advanced strategy for Scopone. Outperforms all the rule-based players that implement The cheating MCTS player outperforms all the other strategies while the fair ISMCTS player Then, we performed a tournament among the three selected players and also an experiment involving humans.
We performed a set of experiments to select the best rule-based player and the best configuration for MCTS and ISMCTS. We evaluated different reward functions and simulation strategies. ISMCTS can deal with incomplete information and thus implements a fair player.
MCTS requires the full knowledge of the game state (that is, of the cards of all the players)Īnd thus, by implementing a cheating player, it provides an upper bound to the performance achievable with this class of methods. The third rule-based player extends the previous approach with the additional rules introduced in. The second one implements Chitarella’s rules with the additional rules introduced by Saracino ,Īnd represents the most fundamental and advanced strategy for the game The first rule-based player implements the basic greedy strategy taught to beginner players Players based on Monte Carlo Tree Search (MCTS) Īnd Information Set Monte Carlo Tree Search (ISMCTS).
We compare the performance of three rule-based players that implement well-established playing strategies against In this paper, we present the design of a competitive artificial intelligence for Scopone and The second most important strategy book was written by Cicuti and Guardamagna who enriched īy introducing advanced rules regarding the play of sevens.
Only recently Saracino, a professional bridge player, proposed additional rules to extend the original strategy Īlthough dated, the rules by Chitarella are still considered the main and most important strategy guide for Scopone However several historians argue that Scopone was known centuries before Chitarella. 2 2 2 Īt that time, Capecelatro wrote that the game was known by 3-4 generations, therefore it might have been born in the eighteenth century Was written by Capecelatro in 1855, “Del giuoco dello Scopone”. The first original book about Scopone, that is still available, Unfortunately, there are no copies available of the original book and the eldest reprint is from 1937. The rules of the game and a compendium of strategy rules for advanced players. The first known book about Scopone was published in 1750 by Chitarella and contained both It is often referred to as Scopone Scientifico, that is, Scientific Scopone). Originally, the game was played by poorly educated people andĪs any other card game in Italy at the time was hindered by authority.īeing considered intellectually challenging, the game spread among highly educated people and high rank politicians 1 1 1E.g., Īchieving, nowadays, a vast popularity and a reputation similar to Bridge (for which Scopone is a popular Italian card game whose origins date back to (at least) 1700s.