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Optimisation of MCTS player for The Lord of the Rings: The Card Game

Godlewski Konrad, Sawicki Bartosz

Bulletin of the Polish Academy of Sciences. Technical Sciences

2021

Vol. 69, nr 3

art. no. e136752

The article presents research on the use of Monte-Carlo Tree Search (MCTS) methods to create an artificial player for the popular card game “The Lord of the Rings”. The game is characterized by complicated rules, multi-stage round construction, and a high level of randomness. The described study found that the best probability of a win is received for a strategy combining expert knowledge-based agents with MCTS agents at different decision stages. It is also beneficial to replace random playouts with playouts using expert knowledge. The results of the final experiments indicate that the relative effectiveness of the developed solution grows as the difficulty of the game increases.

The use of heuristic algorithms: a case study of a card game

Lichy K., Mazur M., Stolarek J., Lipiński P.

Journal of Applied Computer Science

2018

Vol. 26, nr 2

107--116

In this paper we introduce the results of an experiment consisting in the creation of artificial intelligence using the heuristic algorithm Monte-Carlo Tree Search and evaluation of its effectiveness in the card game Thousand.

Monte Carlo Tree Search Algorithm for the Euclidean Steiner Tree Problem

Bereta M.

Journal of Telecommunications and Information Technology

2017

nr 4

71--81

This study is concerned with a novel Monte Carlo Tree Search algorithm for the problem of minimal Euclidean Steiner tree on a plane. Given p p p points (terminals) on a plane, the goal is to ﬁnd a connection between all the points, so that the total sum of the lengths of edges is as low as possible, while an addition of extra points (Steiner points) is allowed. Finding the minimum Steiner tree is known to be np-hard. While exact algorithms exist for this problem in 2D, their eﬃciency decreases when the number of terminals grows. A novel algorithm based on Upper Conﬁdence Bound for Trees is proposed. It is adapted to the speciﬁc characteristics of Steiner trees. A simple heuristic for fast generation of feasible solutions based on Fermat points is proposed together with a correction procedure. By combing Monte Carlo Tree Search and the proposed heuristics, the proposed algorithm is shown to work better than both the greedy heuristic and pure Monte Carlo simulations. Results of numerical experiments for randomly generated and benchmark library problems (from OR-Lib) are presented and discussed.