A Case for Personalized Non-Player Character Companion Design

Abstract

Personalized 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 personalized nonplayer character (NPC) 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 personalized companion before detailing a framework for an adaptive system that modifies companion characteristics based on the player’s state. This framework takes a human-centered approach to personalized 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. The framework firstly quantifies the companion’s behaviour according to recent companion design architecture. Following this, the companion’s characteristics are updated according to the player state, previous player states, evaluations of changes, and the player model. The last phase highlights how to evaluate the companion during the design stage to ensure that it is reliable in assisting the player. 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.