460-4152/02 – Artificial Intelligence in Games (UIH)
| Gurantor department | Department of Computer Science | Credits | 4 |
| Subject guarantor | doc. Ing. Lenka Skanderová, Ph.D. | Subject version guarantor | doc. Ing. Lenka Skanderová, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Optional |
| Year | 2 | Semester | winter |
| | Study language | English |
| Year of introduction | 2022/2023 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The subject aims to introduce the fundamental techniques of artificial intelligence in computer games regarding the techniques and algorithms presented in other subjects (Theoretical informatics, Algorithms I and II, Neural networks, Unconventional algorithms and computing, etc.). The subject deals with the crucial topics in AI for PC games as movement, pathfinding, decision making, tactical and strategic AI, learning, and board games. At the end of the semester, students should be capable to select an appropriate technique and apply it within the computer game regardless of the programming platform.
Teaching methods
Lectures
Tutorials
Summary
The fundamental algorithms used in computer games will be introduced. The subject deals with topics as game theory, algorithms used in board games, motion of a character, game physics, pathfinding, decision making and decision algorithms, artificial intelligence in tactical and strategic games, learning.
Compulsory literature:
[1] Millington, Ian, and John Funge. Artificial intelligence for games. CRC Press, 2018.
Recommended literature:
[1] Yannakakis, Georgios N., and Julian Togelius. Artificial intelligence and games. Springer, 2018.
[2] Buckland, Mat. Programming Game AI by Example. Wordware Publishing, Inc., 2005
Way of continuous check of knowledge in the course of semester
The student will implement the selected algorithms. The implementations will be checked continuously.
E-learning
Other requirements
There are no additional requirements.
Prerequisities
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Board games (2 lectures)
- Game theory
- Minmax Search
- Alpha-Beta Search
- Monte Carlo Tree Search
2. Movement (2 lectures)
- Steering behaviors
- Kinematic movement algorithms
- Game physics
- Coordinated movement
3. Pathfinding (2 lectures)
- Bread-first and Depth-First search algorithms
- Dijkstra
- A* - IDA (Iterative Deepening A*), SMA* (Simplified Memory-Bounded A*
- Hierarchical pathfinding
- Multi-agent pathfinding
- Flood fill algorithm
4. Decision making (2 lectures)
- Spatial data structures for (faster) Collision calculations – Multi-resolution maps, quad or octrees, binary space partition - BSP) trees
- Decision trees – ID3, C4.5 CART (Classification And Regression Tree), CHAID (Chi-square automatic interaction detection), MARS (multivariate adaptive regression splines)
- State machines, Stack-based finite state machines
- Fuzzy logic
5. Tactical and strategic AI (2 lectures)
- Waypoint tactics – influence maps
- Tactical analysis – map flooding, convolution filters, Gaussian blur
- Tactical Pathfinding – structuring multi-tier AI
- Coordinated actions
6. Learning (3 lectures)
- Learning basics – N-grams, string matching
- Parameter modification
- Action prediction
- Reinforcement learning
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.