Umělá inteligence pro deskovou hru Sagrada
| Thesis title in Czech: | Umělá inteligence pro deskovou hru Sagrada |
|---|---|
| Thesis title in English: | Artificial Intelligence for the board game Sagrada |
| Academic year of topic announcement: | 2022/2023 |
| Type of assignment: | Bachelor's thesis |
| Thesis language: | |
| Department: | Department of Software and Computer Science Education (32-KSVI) |
| Supervisor: | Adam Dingle, M.Sc. |
| Author: | Ákos Vermes - assigned by the advisor |
| Date of registration: | 15.02.2023 |
| Date of assignment: | 15.02.2023 |
| Guidelines |
| Sagrada is a board game for 2-4 players that first appeared in 2017. In the game, players take turn placing dice on a board to score points. The game is somewhat complex, and the optimal strategy for placing dice is not obvious. In this thesis work, the student will implement Sagrada 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. |
| References |
| 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.
Plaat, Aske. Learning to play: reinforcement learning and games. Springer Nature, 2020. 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. |