This course focuses on advanced game AI techniques which can be used to implement a wide range of behaviours, from
navigating difficult terrain to controlling units in real-time strategy games. An emphasis is placed on real-world use-
cases.
Last update: Holan Tomáš, RNDr., Ph.D. (16.05.2025)
Předmět je zaměřen na pokročilé techniky umělé inteligence, které mohou být použity na implementaci široké škály
chování, od navigace složitého terénu po ovládání jednotek ve strategických hrách běžících v reálném čase. Důraz je
kladen na příklady z praxe.
Last update: Holan Tomáš, RNDr., Ph.D. (16.05.2025)
Aim of the course -
To gain an overview of algorithms and techniques commonly used in various kinds of games.
Last update: Holan Tomáš, RNDr., Ph.D. (16.05.2025)
Získat přehled o algoritmech a přístupech běžně používaných v různých typech her.
Last update: Holan Tomáš, RNDr., Ph.D. (16.05.2025)
Course completion requirements -
The course ends with successfully completing an exam and gaining a credit from the labs.
The credit from the labs is not required for taking the exam.
To gain a credit from labs, an active participation on labs is required as well as an implementation of chosen algorithm presented during lectures.
Last update: Holan Tomáš, RNDr., Ph.D. (16.05.2025)
Předmět je zakončen úspěšným složením zkoušky a získáním zápočtu.
Ke složení zkoušky není nutné získat zápočet.
K získání zápočtu se požaduje aktivní participace na cvičení a implementace vybraného algoritmu prezentovaného v rámci přednášky.
Last update: Dvořák Tomáš, doc. RNDr., CSc. (16.05.2025)
Literature -
ADIL, Khan, et al. State-of-the-art and open challenges in RTS game-AI and Starcraft. International Journal of Advanced Computer Science & Applications, 2017, 8.12: 16-24.
BROWNE, Cameron B., et al. A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 2012, 4.1: 1-43.
CHURCHILL, David, et al. Starcraft bots and competitions. In: Encyclopedia of Computer Graphics and Games. Cham: Springer International Publishing, 2024, pp. 1742-1759.
COULOM, Rémi. Efficient selectivity and backup operators in Monte-Carlo tree search. In: 5th International Conference on Computer and Games. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 72-83.
FIORINI, Paolo; SHILLER, Zvi. Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research 17.7 (1998): 760-772.
GOLDBERG, Andrew V; HARRELSON, Chris. Computing the shortest path: A* search meets graph theory. SODA '05: Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 156 - 165.
HARABOR, Daniel; GRASTIEN, Alban. The JPS pathfinding system. Proceedings of the International Symposium on Combinatorial Search. Vol. 3. No. 1. 2012.
ONTANÓN, Santiago, et al. RTS AI problems and techniques. In: Encyclopedia of Computer Graphics and Games. Cham: Springer International Publishing, 2024, pp. 1595-1605.
ORKIN, Jeff. Three states and a plan: the AI of FEAR. In: Game Developers Conference. San Jose, California: CMP Game Group, 2006. p. 4.
RABIN, Steven (ed.). Game AI Pro: Collected Wisdom of Game AI Professionals. CRC Press, 2013.
REYNOLDS, Craig W. Steering behaviors for autonomous characters. In: Game Developers Conference, vol. 1999, pp. 763-782. 1999.
RUSSELL, Stuart J.; NORVIG, Peter, 2020. Artificial Intelligence: A Modern Approach (4th Edition). Pearson. ISBN 978-0134610993.
SILVER, David, et al. Mastering the game of Go without human knowledge. Nature 550.7676 (2017): 354-359.
SILVER, David, et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362.6419 (2018): 1140-1144.
ŠUSTR, Z., et al. MetaCentrum, the Czech Virtualized NGI. In: EGEE Technical Forum. 2009.
ŚWIECHOWSKI, Maciej, et al. Monte Carlo tree search: A review of recent modifications and applications. Artificial Intelligence Review, 2023, 56.3: 2497-2562.
VAN DER BURG, Jur; LIN, Ming; MANOCHA, Dinesh. Reciprocal velocity obstacles for real-time multi-agent navigation. 2008 IEEE International Conference on Robotics and Automation. IEEE, 2008.
Last update: Dvořák Tomáš, doc. RNDr., CSc. (18.05.2025)
ADIL, Khan, et al. State-of-the-art and open challenges in RTS game-AI and Starcraft. International Journal of Advanced Computer Science & Applications, 2017, 8.12: 16-24.
BROWNE, Cameron B., et al. A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 2012, 4.1: 1-43.
CHURCHILL, David, et al. Starcraft bots and competitions. In: Encyclopedia of Computer Graphics and Games. Cham: Springer International Publishing, 2024, pp. 1742-1759.
COULOM, Rémi. Efficient selectivity and backup operators in Monte-Carlo tree search. In: 5th International Conference on Computer and Games. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 72-83.
FIORINI, Paolo; SHILLER, Zvi. Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research 17.7 (1998): 760-772.
GOLDBERG, Andrew V; HARRELSON, Chris. Computing the shortest path: A* search meets graph theory. SODA '05: Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 156 - 165.
HARABOR, Daniel; GRASTIEN, Alban. The JPS pathfinding system. Proceedings of the International Symposium on Combinatorial Search. Vol. 3. No. 1. 2012.
ONTANÓN, Santiago, et al. RTS AI problems and techniques. In: Encyclopedia of Computer Graphics and Games. Cham: Springer International Publishing, 2024, pp. 1595-1605.
ORKIN, Jeff. Three states and a plan: the AI of FEAR. In: Game Developers Conference. San Jose, California: CMP Game Group, 2006. p. 4.
RABIN, Steven (ed.). Game AI Pro: Collected Wisdom of Game AI Professionals. CRC Press, 2013.
REYNOLDS, Craig W. Steering behaviors for autonomous characters. In: Game Developers Conference, vol. 1999, pp. 763-782. 1999.
RUSSELL, Stuart J.; NORVIG, Peter, 2020. Artificial Intelligence: A Modern Approach (4th Edition). Pearson. ISBN 978-0134610993.
SILVER, David, et al. Mastering the game of Go without human knowledge. Nature 550.7676 (2017): 354-359.
SILVER, David, et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362.6419 (2018): 1140-1144.
ŠUSTR, Z., et al. MetaCentrum, the Czech Virtualized NGI. In: EGEE Technical Forum. 2009.
ŚWIECHOWSKI, Maciej, et al. Monte Carlo tree search: A review of recent modifications and applications. Artificial Intelligence Review, 2023, 56.3: 2497-2562.
VAN DER BURG, Jur; LIN, Ming; MANOCHA, Dinesh. Reciprocal velocity obstacles for real-time multi-agent navigation. 2008 IEEE International Conference on Robotics and Automation. IEEE, 2008.
Last update: Dvořák Tomáš, doc. RNDr., CSc. (18.05.2025)
Teaching methods -
Various algorithms and case studies will be presented during the lectures and labs. The students will be tasked with implementing some of the discussed algorithms and coming up with their own AI implementation for a game of their choice. The latter task will be done in teams.
Last update: Holan Tomáš, RNDr., Ph.D. (16.05.2025)
V rámci přednášek a cvičení budou prezentovány různé algoritmy a případové studie. Studenti budou mít za úkol implementovat některé z probíraných algoritmů a v týmu navrhnout vlastní implementací AI pro hru dle vlastního výběru.
Last update: Dvořák Tomáš, doc. RNDr., CSc. (16.05.2025)
Syllabus -
AI agent architectures, game state representations, forward model
