A Case for Personalised Non-Player Character Companion Design

Abstract

Personalised video games have the potential to provide unique and meaningful experiences for the player. User data taken from biosensors, questionnaires, or in-game performance data can infer a player’s psychological state, to which relevant game features can be adapted to enhance the player experience. This survey discusses the data types, game elements, and methods that have been used thus far to create adaptive experiences in games. The survey specifically focuses on personalised non-player characters (NPCs) companions through adaptation. Studies using performance data, affect and cognition, and self-reports to adapt companions and other NPC types are reviewed for their success in providing an enhanced experience. We then provide a motivation for a personalised companion based on recent design frameworks before detailing an adaptive system that modifies companion characteristics based on the player’s internal state. This framework takes a human-centered approach to personalised companion design; it proposes the game elements appropriate for adaptation, the data types that suit the adaptation of the companion type, the techniques that would enable successful adaptation, and methods for companion evaluation. We suggest this as a starting point for game designers when considering how to approach a companion that aims to enhance and sustain player experience.

Keywords: Adaptive games, non-player character, non-player character companion, sensor data, User Experience