{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T20:38:23Z","timestamp":1762547903700,"version":"build-2065373602"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIIDE"],"abstract":"<jats:p>Dynamic Difficulty Adjustment (DDA) systems procedurally\ntune video games during runtime to deliver a\ndesigner-specified, balanced gameplay experience for\nplayers. DDA systems have been well-studied and deployed in\nboth academic and industry contexts. However, a relatively\nunexplored challenge of DDA systems is how to assess the\ndiversity of experiences they can deliver to players and\nwhether or not the range of possible DDA actions will\nsatisfy the original game designer's goals. DDA systems are\ninherently unpredictable in their output due to being\ndesigned to react to game-states that are uncertain and\never-changing. The varying scope and unpredictability of\nDDA systems means human playtesting can be time- and\ncost-intensive, and automated playtesting may produce\nmisleading results. In this work, we introduce an approach\nfor using expressive range analysis and unsupervised\nclustering to explore and evaluate gameplay traces from an\nin-development DDA system (FighterDDA) for turn-based\nrole-playing game encounters. We find that this is an\neffective method for assessing designer goals and re-tuning\naccordingly an in-development system by visualizing and\nunderstanding the character of different DDA approaches.\nWhile specific to this system, we believe that there is\npromise in extending this approach to other genres and DDA\nplatforms in the future.<\/jats:p>","DOI":"10.1609\/aiide.v21i1.36835","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T20:33:48Z","timestamp":1762547628000},"page":"315-325","source":"Crossref","is-referenced-by-count":0,"title":["Designer Difficulties: Visualizing the Possibility Spaces of Dynamic Difficulty Adjustment Systems"],"prefix":"10.1609","volume":"21","author":[{"given":"Samuel","family":"Shields","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Withington","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edward","family":"Melcer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2025,11,7]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIIDE\/article\/download\/36835\/38973","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIIDE\/article\/download\/36835\/38973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T20:33:49Z","timestamp":1762547629000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIIDE\/article\/view\/36835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,11,7]]}},"URL":"https:\/\/doi.org\/10.1609\/aiide.v21i1.36835","relation":{},"ISSN":["2334-0924","2326-909X"],"issn-type":[{"value":"2334-0924","type":"electronic"},{"value":"2326-909X","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}