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Course, academic year 2022/2023
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Procedural Content Generation for Computer Games - NAIL123
Title: Procedurální generování obsahu počítačových her
Guaranteed by: Department of Software and Computer Science Education (32-KSVI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2020
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:1/1, C+Ex [HT]
Capacity: 15
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Guarantor: Mgr. Jakub Gemrot, Ph.D.
Annotation -
Last update: RNDr. Tomáš Holan, Ph.D. (14.05.2019)
The course presents the procedural content generation (PCG) techniques as the means for easing or substituing some of the manual work of game designers. PCG algorithms are all algorithms generating a content for computer games with limited or even no input from their users. We present areas that are suitable for PCG together with example algorithms. Algorithms will be then evaluated according to their speed, reliability, controllability, expressivity, diversity, creativity and believability of generated content.
Aim of the course -
Last update: RNDr. Tomáš Holan, Ph.D. (14.05.2019)

To gain overview about techniques and algorithms used for the proceural content generation in computer games.

Course completion requirements -
Last update: RNDr. Tomáš Holan, Ph.D. (14.05.2019)

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 either selected PCG algorithm presented during lectures or its analysis.

Literature -
Last update: RNDr. Tomáš Holan, Ph.D. (14.05.2019)

Books:

Shaker, N., Togelius, J., & Nelson, M. J. (2016). Procedural content generation in games. Switzerland: Springer International Publishing.

Articles:

Hendrikx, M., Meijer, S., Van Der Velden, J., & Iosup, A. (2013). Procedural content generation for games: A survey. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 9(1), 1.

Togelius, J., Yannakakis, G. N., Stanley, K. O., & Browne, C. (2011). Search-based procedural content generation: A taxonomy and survey. IEEE Transactions on Computational Intelligence and AI in Games, 3(3), 172-186.

Yannakakis, G. N., & Togelius, J. (2011). Experience-driven procedural content generation. IEEE Transactions on Affective Computing, 2(3), 147-161.

Smith, A. M., & Mateas, M. (2011). Answer set programming for procedural content generation: A design space approach. IEEE Transactions on Computational Intelligence and AI in Games, 3(3), 187-200.

Smith, G., Gan, E., Othenin-Girard, A., & Whitehead, J. (2011, June). PCG-based game design: enabling new play experiences through procedural content generation. In Proceedings of the 2nd international workshop on procedural content generation in games (p. 7). ACM.

Johnson, L., Yannakakis, G. N., & Togelius, J. (2010, June). Cellular automata for real-time generation of infinite cave levels. In Proceedings of the 2010 Workshop on Procedural Content Generation in Games (p. 10). ACM.

Teaching methods -
Last update: RNDr. Tomáš Holan, Ph.D. (14.05.2019)

Respective algorithms will be presented theoretically during lectures; these will be implemented and empirically evaluated during labs.

Syllabus -
Last update: RNDr. Tomáš Holan, Ph.D. (14.05.2019)

Taxonomy and metaphores of PCG algorithms

Respective families of PCG algorithms according to their theoretical background:

  • search-based methods;
  • cellular automatons;
  • generative grammars;
  • artificial evolutions;
  • logical programming.

Concrete examples of PCG algorithm usages

Evaluation methods for PCG algorithms

 
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