AI/ML

Tree Search vs Optimization Approaches for Map Generation. (arXiv:1903.11678v2 [cs.AI] UPDATED)



Search-based procedural content generation uses stochastic global
optimization algorithms to search spaces of game content. However, it has been
found that tree search can be competitive with evolution on certain
optimization problems. We investigate the applicability of several tree search
methods to map generation and compare them systematically with several
optimization algorithms, including evolutionary algorithms. For purposes of
comparison, we use a simplified map generation problem where only passable and
impassable tiles exist, three different map representations, and a set of
objectives that are representative of those commonly found in actual level
generation problem. While the results suggest that evolutionary algorithms
produce good maps faster, several tree search methods can perform very well
given sufficient time, and there are interesting differences in the character
of the generated maps depending on the algorithm chosen, even for the same
representation and objective.

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