Pivoting the player: a framework for player character research in offline computer role-playing games

This article introduces a theoretical framework for the analysis of the player character (PC) in offline computer role-playing games (cRPGs). It derives from the assumption that the character constitutes the focal point of the game, around which all the other elements revolve. This underlying observation became the foundation of the Player Character Grid and its constituent Pivot Player Character Model, a conceptual framework illustrating the experience of gameplay as perceived through the PC’s eyes. Although video game characters have been scrutinised from many different perspectives, a systematic framework has not been introduced yet. This study aims to fill that void by proposing a model replicable across the cRPG genre. It has been largely inspired by Anne Ubersfeld’s semiological dramatic character research implemented in Reading Theatre I (1999) and is demonstrated with reference to The Witcher (CD Projekt RED 2007).

Od Pacmana do Lary Croft. Jak badać postać w grach wideo?

This article offers an overview of various approaches, which have been used to examine video game characters. In its first part I am introducing several methodological directions, focusing on: characters as functions, characters as drivers of agency, representational gendered icons, and as players’ re-embodied realisations. In the second part I am focusing on the first holistic research method for player character in offline computer role-playing games (cRPGs). The proposed Pivot Player Character Model provides a method replicable across the cRPG genre and illustrates the experience of gameplay as perceived through the PC’s eyes. It has been largely inspired by Anne Ubersfeld’s semiological dramatic character research.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.