Daylamani-Zad, Damon, Agius, Harry and Angelides, Marios C. (2018) Reflective agents for personalisation in collaborative games. Artificial Intelligence Review, 53 (1). pp. 429-474. ISSN 0269-2821 (Print), 1573-7462 (Online) (doi:https://doi.org/10.1007/s10462-018-9665-8)

Preview

PDF (Publisher's PDF - Open Access) 22121 DAYLAMANI-ZAD_Reflective_Agents_for_Personalisation_(OA)_2018.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview

Abstract

The collaborative aspect of games has been shown to potentially increase player performance and engagement over time. However, collaborating players need to perform well for the team as a whole to benefit and thus teams often end up performing no better than a strong player would have performed individually. Personalisation offers a means for improving overall performance and engagement, but in collaborative games, personalisation is seldom implemented, and when it is, it is overwhelmingly passive such that the player is not guided to goal states and the effectiveness of the personalisation is not evaluated and adapted accordingly. In this paper, we propose and apply the use of reflective agents to personalisation (‘reflective personalisation’) in collaborative gaming for individual players within collaborative teams via a combination of individual player and team profiling in order to improve player and thus team performance and engagement. The reflective agents self-evaluate, dynamically adapting their personalisation techniques to most effectively guide players towards specific goal states, match players and form teams. We incorporate this agent-based approach within a microservices architecture, which itself is a set of collaborating services, to facilitate a scalable and portable approach that enables both player and team profiles to persist across multiple games. An experiment involving 90 players over a two-month period was used to comparatively assess three versions of a collaborative game that implemented reflective, guided, and passive personalisation for individual players within teams. Our results suggest that the proposed reflective personalisation approach improves team player performance and engagement within collaborative games over guided or passive personalisation approaches, but that it is especially effective for improving engagement.

Item Type:	Article
Additional Information:	© The Author(s) 2018. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords:	Personalisation, Profiling, Collaborative games, Decision-making games, Reflective agents, Player engagement, Player performance
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	04 Mar 2022 13:06
URI:	http://gala.gre.ac.uk/id/eprint/22121

Actions (login required)

View Item

Downloads