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Difficulty balancing methods in simulators with leveling systems

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dc.contributor.authorVyshnivska, V. V.en
dc.contributor.authorKovalenko, O. O.en
dc.date.accessioned2026-09-03T10:52:04Z
dc.date.available2026-09-03T10:52:04Z
dc.date.issued2026
dc.description.abstractThe paper examines difficulty balancing methods in video games and simulators with player leveling systems. Key approaches are described: static and dynamic balancing, adaptive difficulty systems, learning curves and progression design. Their advantages, disadvantages, and applicability to creative activity simulators are analyzed.en
dc.identifier.citationВишнівська В. В., Коваленко О. О. Difficulty balancing methods in simulators with leveling systems // Матеріали Міжнародної науково-практичної інтернет-конференції «Молодь в науці: дослідження, проблеми, перспективи (МН-2026)», м. Вінниця, 22-26 червня 2026 р. Електрон. текст. дані. 2026. URI: https://conferences.vntu.edu.ua/index.php/mn/mn2026/paper/view/29803.uk
dc.identifier.udc004.4:794
dc.identifier.urihttps://ir.lib.vntu.edu.ua/handle/123456789/53782
dc.language.isoen_USen_US
dc.publisherВінницький національний технічний університетuk
dc.relation.ispartofМатеріали Міжнародної науково-практичної інтернет-конференції «Молодь в науці: дослідження, проблеми, перспективи (МН-2026)», м. Вінниця, 22-26 червня 2026 р.uk
dc.relation.references, and skill levels – is the foundation of modern adaptive systems. Models may be explicit (derived from direct measurement) or implicit (inferred from behavioral data). Combining both approaches yields robust profiles that support accurate difficulty calibration [1; 3]. Conclusions Difficulty balancing in leveling simulators is a multifaceted engineering and design challenge that spans static configuration, dynamic adaptation, and intelligent player modeling. The most effective modern approaches combine progression system design with real-time adaptive mechanisms informed by behavioral data. For creative activity simulators in particular, balancing must account for subjective success criteria and the value of player exploration, suggesting that hybrid DDA-PCG systems represent a promising direction. Further research into player modeling for creative domains and the application of machine learning to difficulty calibration will be essential for advancing the field. REFERENCESen
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dc.relation.urihttps://conferences.vntu.edu.ua/index.php/mn/mn2026/paper/view/29803
dc.subjectdifficulty balancingen
dc.subjectsimulatoren
dc.subjectleveling systemen
dc.subjectdynamic difficultyen
dc.subjectadaptive game mechanicsen
dc.subjectgamedesignen
dc.titleDifficulty balancing methods in simulators with leveling systemsen
dc.typeThesis

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Молодь в науці: дослідження, проблеми, перспективи (МН-2026)