A Computational Model of Suspense for Non-Narrative Gameplay

摘要: In recent years, Various mechanisms have been proposed to optimize for players’ emotional experience. this paper, we focus on suspense, one of the key emotions in gameplay. Most previous research suspense management games focused narratives. Instead, propose a new computational model Suspense Non-Narrative Gameplay (SNNG). SNNG is built around Player Model (PSM) with three factors: hope, fear, and uncertainty. These factors are modeled as sensors that can be triggered by particular game objects (e.g., NPCs) mechanics health). A player’s feeling adjusted altering level Therefore, an SNNG-enhanced engine could manage adding or removing objects, diverting NPCs, adjusting mechanics, giving withholding information. We tested our integrating into Pacman game. Our preliminary experiment nine subjects was encouraging.