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en:dydaktyka:ggp:game_tree_1 [2018/01/23 14:36] msl [Ćwiczenia] |
en:dydaktyka:ggp:game_tree_1 [2021/01/12 01:01] msl [Assignments] |
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| {{ :pl:dydaktyka:ggp:tic-tac-toe-tree.png?500 | }} | {{ :pl:dydaktyka:ggp:tic-tac-toe-tree.png?500 | }} | ||
| - | We will use the 'ggp-base' framework, [[en:dydaktyka:ggp:gdl|introduced together with the gdl language]]. | + | We will use the 'ggp-base' framework, [[en:dydaktyka:ggp:gdl#validation|introduced together with the gdl language]]. |
| ===== First Legal Move ===== | ===== First Legal Move ===== | ||
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| - implement timeout handling | - implement timeout handling | ||
| - test the 'checkers' again | - test the 'checkers' again | ||
| - | - bonus assignments | + | - bonus assignment: implement the [[https://thimbleby.gitlab.io/algorithm-wiki-site/wiki/minimax_search/|NegaMax algorithm]] |
| - | - add possibility to cinstraint the search depth (several moves look-ahead) --- it's a classical method to scale the AI power. Why this method may be better than the timeout? | + | |
| - | - implement the [[http://sandbox.thimbleby.net/algorithms/doku.php?id=minimax_search|NegaMax algorithm]] | + | |
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| Ending of [[https://www.youtube.com/watch?v=Ewh-rF7KSEg|the video]] shows an example of the Alpha-Beta pruning. | Ending of [[https://www.youtube.com/watch?v=Ewh-rF7KSEg|the video]] shows an example of the Alpha-Beta pruning. | ||
| - | <WRAP center round tip 60%>[[http://will.thimbleby.net/algorithms/doku.php?id=minimax_search_with_alpha-beta_pruning|Interactive Visualization of the algorithm]]. | + | <WRAP center round tip 60%>[[https://thimbleby.gitlab.io/algorithm-wiki-site/wiki/minimax_search_with_alpha-beta_pruning/|Interactive Visualization of the algorithm]]. |
| </WRAP> | </WRAP> | ||
