Comparison of search algorithms in strategy video game problems
Strategy games have always been very liked by people. Games such as chess and checkers, to mention a few that are widely played, have created rivalry between players and have even become sports played by professionals. With the rise of artificial intelligence, a new window opens to test whether comp...
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| Tác giả khác: | , , |
| Định dạng: | article |
| Ngôn ngữ: | Tiếng Tây Ban Nha |
| Được phát hành: |
2020
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| Truy cập trực tuyến: | https://revistas.utp.ac.pa/index.php/ric/article/view/2897 https://ridda2.utp.ac.pa/handle/123456789/12189 |
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| Tóm tắt: | Strategy games have always been very liked by people. Games such as chess and checkers, to mention a few that are widely played, have created rivalry between players and have even become sports played by professionals. With the rise of artificial intelligence, a new window opens to test whether computers can outperform humans. From these ideas, algorithms have emerged that seek to solve problems through different strategies. In this article, the minimax and alpha-beta algorithms, their definition, functionality and complexity were studied. These algorithms were implemented in the emulation of the Lara Croft Go game, which was programmed in the Java language using the NetBeans IDE. The application consists of a game where a human faces an AI that was programmed with both algorithms and is defined by different factors, who is the best solving the game. Data collection is defined by the movements that each algorithm makes to complete the levels and the number of states it generates in the search for the solution. Based on the data collected during the execution of the algorithms, it is shown that the most efficient algorithm in finding the solution of the implemented levels is the Alpha-Beta search algorithm, since it generates fewer states, therefore, it evaluates less nodes and reaches a solution in less time than the Minimax search algorithm. |
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