{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/monte-carlo-methods-for-the-game-kingdomino","title":"Monte Carlo Methods for the Game Kingdomino","arxiv_id":"1807.04458","date":"2018-07-12","proceeding":null,"authors":["Magnus Gedda","Mikael Z. Lagerkvist","Martin Butler"],"abstract":"Kingdomino is introduced as an interesting game for studying game playing:\nthe game is multiplayer (4 independent players per game); it has a limited game\ndepth (13 moves per player); and it has limited but not insignificant\ninteraction among players.\n  Several strategies based on locally greedy players, Monte Carlo Evaluation\n(MCE), and Monte Carlo Tree Search (MCTS) are presented with variants. We\nexamine a variation of UCT called progressive win bias and a playout policy\n(Player-greedy) focused on selecting good moves for the player. A thorough\nevaluation is done showing how the strategies perform and how to choose\nparameters given specific time constraints. The evaluation shows that\nsurprisingly MCE is stronger than MCTS for a game like Kingdomino.\n  All experiments use a cloud-native design, with a game server in a Docker\ncontainer, and agents communicating using a REST-style JSON protocol. This\nenables a multi-language approach to separating the game state, the strategy\nimplementations, and the coordination layer.","url_abs":"http://arxiv.org/abs/1807.04458v2","url_pdf":"http://arxiv.org/pdf/1807.04458v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"monte-carlo-methods-for-the-game-kingdomino","repo_url":"https://github.com/mgedda/kdom-ai","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}