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Constrained novelty search: a study on game content generation.

Antonios Liapis1, Georgios N Yannakakis, Julian Togelius

  • 1Center for Computer Games Research, IT University of Copenhagen, Copenhagen, 2300, Denmark anli@itu.dk.

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Summary
This summary is machine-generated.

Constrained novelty search algorithms explore feasible and infeasible spaces using two populations. These methods generate more diverse game levels than existing approaches, with offspring boosting enhancing performance.

Keywords:
Genetic algorithmscomputer gamesconstrained optimizationlevel designnovelty searchprocedural content generationtwo-population genetic algorithm

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Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation
  • Procedural Content Generation

Background:

  • Novelty search explores objective-free search spaces.
  • Constraints divide search spaces into feasible and infeasible regions, posing challenges for exploration.
  • Existing novelty search methods may struggle with constrained optimization problems.

Purpose of the Study:

  • To address the problem of constrained novelty search.
  • To propose and evaluate novel algorithms for exploring both feasible and infeasible search spaces.
  • To enhance the generation of diverse and playable game levels through constrained novelty search.

Main Methods:

  • Developed two novelty search algorithms inspired by the FI-2pop genetic algorithm.
  • Each algorithm maintains and evolves separate feasible and infeasible populations.
  • Applied algorithms to procedural game content generation, specifically diverse game level creation, incorporating an offspring boosting enhancement.

Main Results:

  • The proposed two-population constrained novelty search methods generated larger and more diverse sets of feasible game levels compared to existing methods.
  • Performance varied based on search space characteristics and genetic operators.
  • Offspring boosting consistently enhanced the performance of two-population novelty search.

Conclusions:

  • Two-population novelty search effectively handles constrained search spaces for problems like procedural content generation.
  • Algorithm selection is dependent on specific search space properties.
  • Offspring boosting is a valuable enhancement for two-population novelty search strategies.