aiqbench analysis
This research addresses a significant gap in Procedural Content Generation by introducing novel methods for automatically creating enemy collision morphologies in video games. The study demonstrates that these new approaches match or exceed the performance of evolutionary baselines derived from robotics, offering distinct trade-offs for game developers. While technically interesting, the work remains a niche application within AI rather than a broad infrastructure advancement.
An Exploration of Collision-based Enemy Morphology Generation
arXiv:2606.02832v1 Announce Type: new Abstract: Despite a great deal of prior research into Procedural Content Generation (PCG), relatively little prior work has explored generating enemies for video games. In particular, there is almost no work on generating enemy morphologies, the basic body plan or collision information for in-game enemies, despite the existence of related morphology generation work in robotics. In this paper, we explore three different novel approaches to generate enemy morphologies based on player collision information. We found that each approach provides different strengths and weaknesses, but all had equivalent or better performance than an evolutionary baseline adapted from prior robotics morphology work.
Abstract:Despite a great deal of prior research into Procedural Content Generation (PCG), relatively little prior work has explored generating enemies for video games. In particular, there is almost no work on generating enemy morphologies, the basic body plan or collision information for in-game enemies, despite the existence of related morphology generation work in robotics. In this paper, we explore three different novel approaches to generate enemy morphologies based on player collision information. We found that each approach provides different strengths and weaknesses, but all had equivalent or better performance than an evolutionary baseline adapted from prior robotics morphology work.
Artificial Intelligence (cs.AI)
arXiv:2606.02832 [cs.AI]
(or arXiv:2606.02832v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.02832 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 193 words