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Progressive
Content
Generation
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.
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.
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
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
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.
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.
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
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.
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
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.
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
Customizables
The Enemy Spawn Time
Damage Points and Health
Time Duration
Enemy Capabilities - Power, Strength, Frequency of Attacks
Player Capabilities
Powerups - Time Frequency
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
Thank you

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Progressive content generation

  • 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