During the game, the players can build settlements and cities on the vertices of the hexagons, and roads on the edges. There is a robber on the island, initially residing in the desert. Several of the sea tiles surrounding the island contain a port. Each non-desert tile has a production number. There is also one desert which does not produce anything. Each tile rep- resents a field that provides one of the five types of resources : wood, clay, sheep, wheat or ore. Game board and resources : The game board representing the island is randomly assembled from 19 hexagonal tiles forming a large hexagon (Figure 1 displays an example game board in our SmartSettlers program). Below, we summarize the rules (omitting many details). Detailed descriptions of the rules can be easily found on the internet. The goal of the game is to be the first player who gains at least 10 victory points. They use these resources to build new settlements, cities, roads, and developments. They collect resources by suitable positioning of their settlements. In Settlers of Catan, the players take the role of settlers inhabiting an island. The game achieved a huge success: it received the “Game of the Year” award of the German game critics, and it was the first “eurogame” to become widely popular outside Germany, selling more than 11 million copies, inspiring numerous extensions, successors, and computer game versions. Settlers of Catan, designed by Klaus Teuber, was first published in 1995. In this paper, we investigate whether it is possible to use one of the AI tools of classical board games, namely Monte-Carlo Tree Search, effectively for implementing game-playing agents for games like. Few research papers are avail- able on autonomous learning in Settlers of Catan, and according to the results reported therein, they are far from reaching human-level play yet. The strength of these AIs varies, but an experienced player can defeat them easily. Several computer implementations of Settlers of Catan exist, which typically feature a hand-designed, rule-based AI. On the other hand, the gameplay in modern board games often incorporates elements of randomness, hidden information, multiple players, and a variable initial setup, which make it hard to use classical techniques such as alpha-beta pruning or opening books. On the one hand, state variables of most modern board games are discrete, and decision making is turn-based. Strategic board games are of particular interest to AI researchers because they provide a direct link between classic (two-player, perfect information) board games and video games. The game Settlers of Catan can be considered an archetypical member of the genre. Modern strategic board games (sometimes called “eurogames”) are increasing in popularity since their (re)birth in the 1990’s. Games are good indicators and often-used benchmarks of AI performance: Chess, Checkers, Backgammon, Poker and Go all define important cornerstones of the development of artificial intelligence. Nevertheless, the complexity is high enough to make them appealing to human intelligence. Abstraction makes games easier to analyze than real-life environments, and usually provides a well-defined measure of performance. Most games are abstract environments, intended to be interesting and chal- lenging for human intelligence. This is especially true when we take into account the role of games in human so- cieties: it is generally believed that games are tools both for children and adults for understanding the world and for developing their intelligence. In this respect, games (including the diverse set of board games, card games and modern computer games) are considered to be ideal test environments for AI research. consensus states that a learning agent must be situated in an experience- rich, complex environment for the emergence of intelligence.
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