Lu-Lu: A framework for collaborative decision making games

Abstract

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.

Reflective agents for personalisation in collaborative games

“…While a progress map is included in the game, it serves only as a visual reference for players, and while the intelligent agent operates according to predefined goals, it does not seek to progress the players or teams to specific states. Similarly, in our earlier Lu-Lu architecture (Daylamani-Zad et al 2016), messages are sent to players in order to encourage engagement and performance. These are triggered by decision trees which do not take player's historical activities into account.…”

“…In this section, we propose an architecture for reflective personalisation for collaborative games. To do so, we build upon our previous Lu-Lu architecture (Daylamani-Zad et al 2016), which fostered collaboration through the implementation of collaborative features such as team matching, leadership, non-optimality, identity awareness, and passive personalisation. The architecture incorporates multiple reflective agents to facilitate reflective personalisation and aims to work with games that have existing collaborative capabilities.…”

“…This combination aims to create effective teams in order to increase player engagement and promote performance. The Team Profile, presented in Table 4, is used to support collaboration and for player matching and dynamic team formation and re-formation, including leadership, based on a team status of balanced, imbalanced, or ineffective, as described in Daylamani- Zad et al (2016). As discussed in Sect.…”

“…The team leader among players is selected as the player with the highest decisiveness index within the team by considering the score and level of all players in the team as suggested by Yang et al (2015) and Daylamani-Zad et al (2016). The player with the highest combination of level and score is announced as the leader (the most engaged and best performing player in the team).…”

“…The aim of this paper is to apply personalisation in collaborative gaming to individual players within teams via a combination of individual player and team profiling with the intention of improving team player performance and engagement. Previously (Daylamani-Zad et al 2016), we have experimented with personalisation via individual and team profiling with positive results in terms of player enjoyment and decision making. This paper extends that work by utilising reflective agents (Brazier and Treur 1999;Russell and Norvig 2009) for personalisation ('reflective personalisation'), such that players are actively guided to suitable goal states via a self-evaluative technique that constantly monitors the impacts of recommendations and adapts future recommendations accordingly, including the matching of players and the dynamic formation of teams.…”