Enhancing Drive-Based NPC Behavior with Panksepp-Inspired Affective Systems
Resumo
Introduction: Non-player characters (NPCs) that rely on fixed scripts often exhibit repetitive and non-adaptive behavior, reducing immersion in interactive environments. Recent approaches incorporate affective and motivational models, but often lack reproducibility and robustness. Objective: This paper investigates whether integrating Panksepp-inspired primary affective systems into a drive-based neurochemical NPC architecture improves behavioral robustness, stability, and diversity. Survival is treated as a measure of homeostatic robustness rather than as direct evidence of player-perceived believability. Method: We reformulate a previous Godot-based model into a deterministic Python simulation and introduce two architectural variants incorporating Panksepp’s affective systems. The models are evaluated using multi-seed experiments (30 seeds) and parameter sensitivity analysis. Results: The deterministic baseline improves survival from 62% to 87.87%. The Panksepp-based models further increase survival to approximately 99%, while also reducing variance across seeds. However, behavioral imbalances such as reduced aggression emerge. The inclusion of an intermediate affective layer improves robustness and interpretability, but requires multi-objective tuning to preserve behavioral diversity.
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