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This article presents an agent-based testing framework called\n                    <jats:sans-serif>aplib<\/jats:sans-serif>\n                    that comes with a Domain Specific Language (DSL) that allows complex playtests to be programmed more abstractly. A so-called goal structure is used to abstractly formulate a playtest scenario in terms of main goals and their decomposition into subgoals. Scenarios that are not too complicated can be formulated using static goal structures. More complex scenarios may need a test agent that can dynamically adapt its play according to the situation that evolves during the play. To handle such cases,\n                    <jats:sans-serif>aplib<\/jats:sans-serif>\n                    allows dynamic goals to be expressed as well. Invariants and pre-\/post-conditions are used to assert the properties that a play is expected to satisfy. They include differential properties that allow constraints on the current state to be related to that of past states. Three case studies are included in the article. The first one aims to evaluate the performance of playtests programmed with\n                    <jats:sans-serif>aplib<\/jats:sans-serif>\n                    . The second shows that the approach can also be combined with other automated testing approaches, in this case reinforcement learning. The third shows the applicability of such playtests in a 3D setup and for non-functional testing.\n                  <\/jats:p>","DOI":"10.1145\/3742473","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T10:41:24Z","timestamp":1748947284000},"page":"1-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Programming Smart Playtesting"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3421-4635","authenticated-orcid":false,"given":"I. S. W. 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