A player evolves in a game by playing moves, he seeks to play the best move in every situation to win the game. Tree-search is an artificial intelligence technique that simulates many moves, evaluates each discovered game state and pick the moves leading to the best ones. Goal generation is another technique where the current game state is evaluated against multiple strategies and the one evaluated to yield the best promising line of play is picked. In this work we explain the two techniques along with a few others commonly used in game solvers and describe how they can be applied to solve Freecell, a solitaire card game that is played by millions world-wide.
| Date of Award | 23 Jun 2021 |
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| Original language | English |
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| Awarding Institution | |
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| Supervisor | Jean-Marie Jacquet (Supervisor) |
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- artificial intelligence
- tree-search
- goal generation
- reinforcement learning
- game solver
- solitaire
- freecell
So you want to solve Freecell ? An academic journey into crafting a solitaire game solver
Castiaux, J. (Author). 23 Jun 2021
Student thesis: Master types › Master in Computer science