International Journal of Science, Engineering and TechnologyAuthors: Rajan Kumar, Vaibhav Verma, Bishwajit Andia, Sakshi Singh
Abstract: Non-Player Characters (NPCs) in Interactive Digital Environments have traditionally been limited by their artificial script, repetitive loops of actions, and primarily lack in psychological depth. The standards players have for NPCs are increasingly advanced, and the role of creating narrative is becoming a key quality indicator for games, making a level of memory, emotions, moral reasoning and strategic adaptations a requirement at this level. The authors introduce a comprehensive framework for research and development of four aspects of next-generation NPC intelligence: Emotional Memory and Relational Persistence, Moral Fatigue and Ethical Exhaustion, Adaptive Villain Intelligence, and Crowd Behavioural Authenticity. This research builds on the theories and computational paradigms of Large Language Models (LLMs) [4,12], Deep Reinforcement Learning (DRL) [13] and hybrid Behaviour Tree architectures [11] to create the theoretical and engineering underpinning needed to develop NPCs that go beyond the traditional role of a background character. Experimental comparisons conducted by [8] show that DRL-based antagonists can give rise to significantly higher player satisfaction and immersion scores when compared to Finite State Machine (FSM) based ones. In addition, the LLM-based crowd agents had better behavioural understandability scores according to the related crowd simulation studies. The paper ends with a few recommendations for researchers who wish to put forth original scientific work in the fast-changing field of Game Artificial Intelligence.
DOI: http://doi.org/
