Umělá inteligence pro deskovou hru Sagrada
| Název práce v češtině: | Umělá inteligence pro deskovou hru Sagrada |
|---|---|
| Název v anglickém jazyce: | Artificial Intelligence for the board game Sagrada |
| Akademický rok vypsání: | 2022/2023 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | |
| Ústav: | Katedra softwaru a výuky informatiky (32-KSVI) |
| Vedoucí / školitel: | Adam Dingle, M.Sc. |
| Řešitel: | Ákos Vermes - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 15.02.2023 |
| Datum zadání: | 15.02.2023 |
| Datum potvrzení stud. oddělením: | 23.11.2023 |
| Zásady pro vypracování |
| 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. |
| Seznam odborné literatury |
| 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. |