Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games. However, in more complex games (e.g. those with a high branching factor or real-time ones) as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey was published in 2012. Contributions that appeared since its release are of particular interest for this review.

The article presents the use of Monte Carlo Tree Search algorithms for the card game Lord of the Rings. The main challenge was the complexity of the game mechanics, in which each round consists of 5 decision stages and 2 random stages. To test various decision-making algorithms, a game simulator has been implemented. The research covered an agent based on expert rules, using flat Monte-Carlo search, as well as complete MCTS-UCB. Moreover different playout strategies has been compared. As a result of experiments, an optimal (assuming a limited time) combination of algorithms were formulated. The developed MCTS based method have demonstrated a advantage over agent with expert knowledge.

they are an effective, modern educational tool [13], e.g., in medicine [14]. Cooperation allows to learn teamwork, and this is often not possible without the additional presence of artificial players with decision making skills. The main difficulty in cooperative games is created by a complex, multi-stage round structure, which contains several unpredictable random events. Cooperative games make it also possible to play solo, which was analysed in this research.An interesting approach for MCTS application in collectible card games has been presented in 2019 using the "Hearthstone" game [15]. Authors identified huge size of the action space. Several precautions had been taken to reduce number of allowed moves. Using Action filtering and Obliged Actions are examples of successful domain-specific knowledge incorporation.This article is directly based on authors conference work [16]. The algorithm has been extended by adding more expert knowledge into the standard MCTS implementation. This allowed to perform a new analysis for games with high complexity level. We have demonstrated that the relative effectiveness of the mixed strategy proposed in [16] rises as the difficulty of the problem increases. The Lord of the Rings: The Card Game"The Lord of the Rings: The Card Game", often abbreviated as LoTR, is a complicated cooperative card game with several decision-making stages. The following section is devoted to the presentation of its basic rules. It is necessary to understand before we will discuss construction of the game simulator, and searching for the optimal strategy for the AI player. Living Card Game.From the Poker to the "Magic: the Gathering", card games belong to a group of games characterized by hidden information and a high degree of randomness. Hidden information means that the player does not have a complete view of the game, opposing cards