Progressive Content Generation is the process of creating automatic game content through algorithms. This can include decorative elements like skyboxes or complete levels with challenges, rules, and gameplay. Content is generated systematically according to defined behaviors, patterns, and possibilities within the game space. There are different approaches to procedural content generation including constructive, generate-and-test, search-based evolutionary techniques, and level generators. Playability is an important consideration and critical solutions or rewards should be incorporated to ensure generated content is interesting and solvable. While full procedural generation is possible, hand-crafting some portions may be necessary for complex games. An effective PCG system will iteratively generate, test, and regenerate content until requirements are met. Dynamic game
2. Progressive Content Generation
Progressive Content Generation is the process of creating automatic game content
by computers through algorithms.
The content generated could vary from a aesthetical structure like a sky box or tree
generation to a complete level that comprises the challenge, rules and playability.
In short, algorithmic generation of game content.
3. Systemized
Anything that can be generated by algorithms should be systemized.
A system will specify defined behavior, pattern and possibilities.
Every Game Space has an underlying structure.
Technically, a Game Space is set of identical entity called cells.
4. Systems of PCG
Constructive
Unplayable content - like Decorative Elements
Generate and Test
Test for necessary constraints - if not satisfy - regenerate
The content could be Level Balance / Solvability
5. Search Based - Evolutionary
Focus on Evaluation Functions.
Flexible enough to fulfil all the fitness functions (how “fit” our “good” the solution is
with respect to the problem in consideration).
Complex to create and perform slow.
More Playability demands - More Evaluation Function - More Computation Power -
Expensive
6. Level Generators
Consider a Platform Generator.
The generator - Various types of Platforms, platform length, Dynamic objects,
Customizing factors, Flow Graph, No of platforms per room, Level of Difficulty.
With respect to the Genre, the generator provides various functionalities.
7. PCG - Playable
Solution Design - Accommodate Solution Design.
Critical Solution should be defined during generator to make sure the
game is solvable.
Alternate solutions should be crafter in order to make it more interesting -
Little complex.
Include rewards and bonus stuffs and Guiding elements.
8. Pure Procedural ?
We can take the option to hand-craft few portions of the game.
When your generator is not able to do so.
When you cannot deliver the experience exactly through the level design
When your level is complex
9. PCG Generator
1. Evolution
a. Generate content by applying all the rules and constraints
2. Simulator
a. Test the outcomes and if it does not meet the requirement - Pass it back to regeneration
Iterative Development is recommended.
10. Dynamic Game Balancing
Customizing Game Progression with respect to Player Performance
Adjusting Game Play parameters in Real Time
Pre-designed difficulty curve is good, but not inclusive.
Who is your target audience - Newbies vs Pros
11. Consideration
Overall Game design will be decided.
Major consideration in DGB is Balancing the Flow of the Game against the Player
Progression.
Pre-defined curve have no space if player failed to learn a skill.
12. Algorithm to detect Player Progression
An algorithm that analyse player’s performance by tracking various information
such as,
● How quick the player finishes the missions ?
● Reward collections
● No of Kills
● XP / Life
● Powers and Capabilities
● Any contextual measures
Decided by the Designer
13. Customizables
The Enemy Spawn Time
Damage Points and Health
Time Duration
Enemy Capabilities - Power, Strength, Frequency of Attacks
Player Capabilities
Powerups - Time Frequency
14. Technical Side
Intelligent Agents - AI powered Game Manager
Heuristic Functions
Computes the success rate of each solution out of multiple branch of solutions
based on the information we have in the current situation.
Technique to identify the most optimal solution by ranking them.
Neural Networks - Identify Patterns and Forecast
Fuzzy Logics - Degrees of Truth
