Artificial Intelligence for the strategy game Score Four
| Thesis title in Czech: | Umělá inteligence pro strategickou hru Score Four |
|---|---|
| Thesis title in English: | Artificial Intelligence for the strategy game Score Four |
| Key words: | Skóre čtyři|minimax|Prohledávání stromu metodou Monte Carlo|zpětnovazební učení|AlphaZero|umělá neuronová síť |
| English key words: | Score Four|minimax|Monte Carlo tree search|reinforcement learning|AlphaZero|neural network |
| Academic year of topic announcement: | 2022/2023 |
| Thesis type: | Bachelor's thesis |
| Thesis language: | angličtina |
| Department: | Department of Software and Computer Science Education (32-KSVI) |
| Supervisor: | Adam Dingle, M.Sc. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 27.09.2022 |
| Date of assignment: | 27.09.2022 |
| Confirmed by Study dept. on: | 07.10.2022 |
| Date and time of defence: | 10.02.2025 09:00 |
| Date of electronic submission: | 09.01.2025 |
| Date of submission of printed version: | 09.01.2025 |
| Date of proceeded defence: | 10.02.2025 |
| Opponents: | doc. Mgr. Martin Pilát, Ph.D. |
| Guidelines |
| Score Four is a three-dimensional abstract strategy game that is similar to the popular game Connect Four. Although Score Four has existed for decades, there is apparently no published literature about the game, and few computer implementations are available. In this thesis work, the student will implement Score Four and will write several artificial agents that can play the game using a variety of methods, possibly including rules-based heuristics, minimax, Monte Carlo tree search and/or reinforcement learning. The student will compare and analyze the performance of these agents, and may also consider whether it is feasible to solve the game completely (as has been done for Connect Four). |
| References |
| Allis, Louis Victor. "A Knowledge-Based Approach of Connect-Four." J. Int. Comput. Games Assoc. 11.4 (1988): 165.
Browne, Cameron B., et al. "A survey of Monte Carlo tree search methods." IEEE Transactions on Computational Intelligence and AI in games 4.1 (2012): 1-43. Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 2018. Yannakakis, Georgios N., and Julian Togelius. Artificial intelligence and games. New York: Springer, 2018. |