Talakat: Bullet Hell Generation through Constrained Map-Elites

Abstract

We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles. Levels are represented using a domain-specific description language, and search in the space defined by this language is performed by a novel variant of the Map-Elites algorithm which incorporates a feasible- infeasible approach to constraint satisfaction. Simulation-based evaluation is used to gauge the fitness of levels, using an agent based on best-first search. The performance of the agent can be tuned according to the two dimensions of strategy and dexterity, making it possible to search for level configurations that require a specific combination of both. As far as we know, this paper describes the first generator for this game genre, and includes several algorithmic innovations.

The paper will be published in GECCO 2018

Cited by in corpus (9)

• Procedural Content Generation through Quality Diversity
• Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature Dimensions
• Multi-Objective Quality Diversity Optimization
• Generating Levels That Teach Mechanics
• Designer Modeling through Design Style Clustering
• Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem
• Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation
• Tree Search vs Optimization Approaches for Map Generation
• Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design