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Creators/Authors contains: "Genoese-Zerbi, Valentina"

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  1. Serious games are games whose primary purpose is something other than entertainment. A variety of domains have explored the use of serious games to achieve specific practical outcomes in players, including training and healthcare. In previous work, I have leveraged adversarial deep learning methods to create models that effectively and dynamically manage the experiences of users. I intend to develop this work further into serious games applications to adapt serious games during gameplay to support the desired outcome. 
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    Free, publicly-accessible full text available November 7, 2026
  2. In strong story experience management problems, an automated storytelling agent balances player autonomy with narrative structure in the context of an interactive story game world. However, it is possible for the game world to get softlocked in states outside narrative structures specified by the game designer. These states are called dead-ends. In this paper, we revisit adversarial strong story experience management, a framing of the experience management problem that models interactive storytelling as an adversarial game where dead-ends are losses. This framing is adversarial against narrative softlocks, not necessarily the player. We present a novel agent based on adversarial search and deep reinforcement learning, which is trained to avoid dead-ends while preserving player autonomy. We compare our approach to a reactive, narrative plan-based mediation system on a test set of games compatible with current narrative planning techniques. We show that our adversarial architecture outperforms narrative mediation on a suite of dead-end metrics during game trace and breadth-first tests of state transition system exploration, using classical and intentional planning domains. 
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    Free, publicly-accessible full text available November 7, 2026
  3. This paper presents a software library that enumerates the space of a state transition system specified by an action language, visualizes the states and action connections as a graph, and modifies the visualization based on underlying features determined through state and graph analysis. The library is intended as a tool for strong story interactive narrative design. 
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    Free, publicly-accessible full text available November 7, 2026