Personalise your massively multiplayer online game (MMOG) with Artemis

Daylamani-Zad, Damon; Angelides, Marios C.; Agius, Harry

doi:10.1007/s00530-011-0237-x

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Team formation is an important but much neglected aspect of collaborative games, yet it is a widely-explored topic in non-gaming domains, such as in collaborative filtering and recommendation (Retna Raj and Sasipraba 2015;Ghenname et al 2015;Najafabadi and Mahrin 2016), collaborative design (Xu et al 2010), collaborative crowdsourcing (Lykourentzou et al 2016), and expert collaboration in social networks (Basiri et al 2017). However, in multi-player games, the focus has overwhelmingly been on player matching and more so with a view to finding worthy opponents for players to play against rather than suitable players to play with in a team (Daylamani Zad et al 2012). For example, Corem et al (2013) propose a technique for quickly matching opponents with similar skill levels to facilitate fair tournaments, while Tsai (2016) propose a technique for matching competitors in educational multi-player online games.…”

Section: Advantages and Challenges Of Collaborative Gamesmentioning

confidence: 99%

Reflective agents for personalisation in collaborative games

2018

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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.

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Section: Advantages and Challenges Of Collaborative Gamesmentioning

confidence: 99%

Reflective agents for personalisation in collaborative games

2018

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“…It builds upon our previous work in collaboration and personalisation in digital games [66,67]. The game connects to the adjustment zone which interacts directly both with the game and the profiling zone.…”

Section: The Lu-lu Add-on Architecturementioning

confidence: 99%

Lu-Lu: A framework for collaborative decision making games

Daylamani-Zad

Angelides

Agius

2016

Decision Support Systems

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This paper proposes Lu-Lu as an add-on architecture to open MMOGs and social network games, which has been developed to utilise a key set of ingredients that underline collaborative decision making games as reported within the research literature: personalisation, team matching, non-optimal decision making, leading, decisiveness index, scoring, levelling, and multiple stages. The implementation of Lu-Lu is demonstrated as an add on to the classic supply chain beer game, including customisation of Lu-Lu to facilitate information exchange through the Facebook games platform, e.g. Graph API and Scores API. Performance assessment of Lu-Lu using Behaviour Driven Development suggests a successful integration of all key ingredients within Lu-Lu's architecture, yielding autonomous behaviour that improves both player enjoyment and decision making.

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“…Players are now expecting a more personalised gaming experience as personalisation has begun to filter into most games [124]. Research has suggested that game personalisation raises player loyalty and enjoyment, which in turn makes gaming experience a (commercial) success [110,115].…”

Section: Effect On Player Satisfactionmentioning

confidence: 99%

Personalised gaming

Bakkes

Tan

Pisan

2012

Proceedings of the 8th Australasian Conference on Interactive Entertainment: Playing the System

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This article focuses on personalised games, which we define as games that utilise player models for the purpose of tailoring the game experience to the individual player. The main contribution of the article is a motivation for personalised gaming, supported by an extensive overview of scientific literature. The motivatin concerns (a) the psychological foundation, (b) the effect on player satisfaction, (c) the contribution to game development, and (d) the requirement for achieving ambitions. The provided overview of scientific literature goes into the subject of player modelling, as well as eight adaptive components: (1) space adaptation, (2) mission / task adaptation, (3) character adaptation, (4) game mechanics adaptation, (5) narrative adaptation, (6) music / sound adaptation, (7) player matching (multiplayer), and (8) difficulty scaling. In the concluding sections, the relationship to procedural content generation is discussed, as well as the generalisation to other domains.

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Personalise your massively multiplayer online game (MMOG) with Artemis

Cited by 5 publications

References 22 publications

Reflective agents for personalisation in collaborative games

Reflective agents for personalisation in collaborative games

Lu-Lu: A framework for collaborative decision making games

Personalised gaming

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