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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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.

Developmental Dyslexia (DD) is a common language-based learning difficulty which occurs across all cultures. Whilst various interventions are implemented to aid with reading difficulties, research suggests that phonics is still the most promising approach, yet the challenge in this approach has always been keeping pupils engaged and interested. Multisensory approaches have shown promise in keeping pupils engaged but they are time consuming and require high levels of teacher involvement. This paper suggests using 3D environments and gaming technology as a multisensory intervention to aid reading in Dyslexia. The paper proposes an initial framework and indicates the development and evaluation strategy for the framework.

Abstract-this paper investigates the use the swarm intelligence of honey bees to create groups of co-operative AI for an RTS game in order to create and re-enact battle simulations. The behaviour of the agents are based on the foraging and defensive behaviours of honey bees, adapted to a human environment. The groups consist of multiple model-based reflex agents, with individual blackboards for working memory, with a colony level blackboard to mimic the foraging patterns. An agent architecture and environment is proposed that allows for creation of autonomous cooperative agents. The behaviour of agents is then evaluated and their intelligence is tested using an adaptation of Anytime Universal Intelligence Test.