Scholarlink

In the previous chapters we introduced different machine learning paradigms and discussed how they could be leveraged for PCGML. What each of those previous chapters had in common was that they treated training and generation as static processes. That is, model training and content generation were processes that a user would start and then just wait for the outcome.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Mixed-Initiative PCGML

  • 章节

  • 发表日期：

    2025年

查看全文
  • Matthew Guzdial,
  • Sam Snodgrass,
  • Adam Summerville

摘要

In the previous chapters we introduced different machine learning paradigms and discussed how they could be leveraged for PCGML. What each of those previous chapters had in common was that they treated training and generation as static processes. That is, model training and content generation were processes that a user would start and then just wait for the outcome.