by Tan Liying
2025,10(7);
198 Views
Abstract
With the rapid development of digital technologies, intelligent generation of game narratives has become a crucial research direction in artificial intelligence and game design fields. However, existing algorithms lack deep understanding of group psychological mechanisms and struggle to generate authentic and credible multi-character interaction scenarios. Based on social proof effect theory, this study constructs an innovative multi-character interaction group behavior simulation algorithm aimed at enhancing the coherence, authenticity, and user experience of game narratives. The research employs a methodology combining theoretical modeling, algorithm design, and empirical validation. First, an "Environment-Cognition-Society-Behavior" quaternary interaction theoretical framework is constructed, providing in-depth analysis of environmental factors' influence mechanisms on group behavior, including the operational patterns of spatial layout, environmental complexity, and contextual cues. Second, the dynamic evolution mechanisms of social proof effects are systematically explored, revealing the inverted U-shaped relationship between group size and influence propagation, the S-shaped temporal curve characteristics of group behavior convergence, and the moderating role of individual differences in environmental adaptation. Building upon this foundation, a narrative generation algorithm based on Graph Neural Networks and Multi-Agent Reinforcement Learning is designed and implemented. Through the collaborative operation of a social proof intensity calculation engine, multi-character decision coordinator, and dynamic narrative generator, high-quality adaptive narrative creation is achieved. Through large-scale user experience testing involving 180 participants, the study validates the algorithm's effectiveness: compared to traditional methods, narrative logical consistency improved by over 40%, character behavior credibility scores reached 8.5 points, overall user immersion increased by 45%, average gameplay duration increased by 68%, and replay rate reached 73.2%. Algorithm performance testing demonstrates an average response time of only 127 milliseconds, memory usage reduced by 39.8%, CPU utilization decreased by 50.4%, exhibiting excellent scalability and system stability that fully meets industrial-grade application requirements. The research achievements not only provide crucial support for technological innovation in the gaming industry but also establish foundations for application expansion in education and training, social governance, mental health, and other fields, possessing significant theoretical value and broad practical application prospects. This study successfully validates the tremendous potential of psychological theories in artificial intelligence algorithm design, opening new pathways for interdisciplinary integration research.
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