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

Monte-Carlo tree search method for the board game Scotland Yard

In the thesis we learn about the field of artificial intelligence that investigates board games and their program-based solutions. We examine Monte-Carlo tree search algorithm and transfer it to well-known board game Scotland Yard, considering advices from Nijssen and Winands. We focus mainly on the third phase of the algorithm, playout, and decide to implement it in three different ways (from less to more advanced techniques). We compare these three aproaches. We compare the win rates and computation time of simple and advanced methods. We also implement the game to the purpose of automated testing. In this game, detectives play by Monte-Carlo tree search algorithm and Mister X plays in two different ways - random and advanced. We want to test all of these six combinations on a large number of games, compare the results and explain them

Artificial Intelligence in Pursuit-evasion Games, Specifically in the Scotland Yard Game

This research provides a heuristic algorithm for the detectives, who try to collectively capture a criminal known as Mr. X, in the Scotland Yard pursuer-evasion game. In Scotland Yard, a team of detectives attempts to converge on and capture a criminal known as Mr. X. The heuristic algorithm developed in this thesis is designed to emulate human strategies when playing the game. The algorithm uses the current state of the board at each time step, including the current positions of the detectives as well as the last known position of Mr. X. The heuristic algorithm then analyses all of the possible options. The heuristic algorithm then uses a process of elimination to detemine the best possible detective moves by running an appropriately constructed minimum cost flow maximum flow instance. The heuristic algorithm was tested in a series of experiments, in which the algorithm achieved a 57 win rate. This win rate was achieved using a random starting position for each of the pursuer detectives as well as for the evader, Mr. X. When Mr. X started at an easily accessible location, namely position 146, the pursuing detectives were able to capture him 62% of the time. These results show promise for this heuristic in pursuer-evader games like Scotland Yard

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Monte-Carlo Tree Search for the game of Scotland Yard

This paper describes how Monte-Carlo Tree Search (MCTS) can be applied to play the hide-and-seek game Scotland Yard. It is essentially a two-player game in which the players are moving on a graph-based map. We show how limiting the number of possible locations of the hider by using information about the hider’s moves increases the performance of the seekers considerably. We also propose a new technique, called Location Categorization, that biases the possible locations of the hider. The experimental results show that Location Cate-gorization is a robust technique which significantly increases the performance of the seekers in Scotland Yard. Next, we show how to handle the coalition of the seekers in Scotland Yard by using Coalition Reduction. This technique balances each seeker’s participation in the coalition by letting them seek the hider more greedily. Coalition Reduction improves the performance of the seekers significantly. Furthermore, we explain how domain knowledge is incorporated by applying -greedy playouts for the hider and the seekers and move filtering to improve the performance of the hider. Finally, we test the performance of our MCTS program against a commercial Scotland Yard program on the Nintendo DS. The results show that the MCTS-based program plays stronger than this program