Thursday 26 Jan, 1:15, Room G45 or G46 [notice will be displayed on the door]
Macvean, Andrew: Designing Adaptive Exergames for Adolescent Children: Lessons Learned from A Preliminary Study
With evermore children in the Western world adopting a sedentary lifestyle, there is an increased emphasis towards physical activity interventions. Thanks to progressions in ubiquitous technologies, exergames, games that facilitate and encourage exercise, have emerged as a potential means to motivate children to exercise in a context they find both familiar and enjoyable. While early research has shown the potential of the genre, few games have been designed specifically with children in mind, accommodating their unique demands. In our work, we aim to close this gap by investigating how children react to, and make use of an exergame designed specifically with them in mind. The aim is to understand how different demographics of children (gender, gaming background, exercise background, etc) react to a location-aware exergame, in order to in the future build more accurate adaptive exergames, suitable for the specific requirements of children users. In this talk we present the first step towards this, the results of a preliminary study on our location-aware exergame - iFitQuest. Our results show early insight into how different users react to our game, and provide lessons on how to approach the design task.
Totoo, Prabhat: Parallel Haskell implementations of the n-body problem
The n-body problem is a problem of predicting the motion of a system of N bodies that interact with each other gravitationally. N-body algorithms are used in several areas such as molecular dynamics, astrophysics.
Several methods exist to solve the problem including bruteforce body-to-body comparison and the more sophisticated Barnes-Hut algorithm. We look at multicore implementations of the problem for simulation consisting of huge input. The implementations cover the use of 3 different parallel programming models in Haskell: GpH, Par monad and Eden. The performance of these models are compared in terms of changes required to the sequential algorithms and runtime/speedup.
Weallans, Allan: DISTRIBUTED DRAMA MANAGEMENT: BEYOND DOUBLE APPRAISAL IN EMERGENT DRAMA
Emergent drama relies on the autonomous actions of AI character agents. While this has many advantages over a more directorial approach, it is not guaranteed to favour dramatically interesting actions or a "story-like" structure. Using affective impact as a surrogate for drama, Double Appraisal was a step towards encouraging dramatic events in autonomous agents. This talk will briefly outline the limitations of Double Appraisal as implemented, and discuss how Distributed Drama Management builds on the Double Appraisal work to help to produce more structured and dramatically interesting narrative experiences while retaining the autonomy of the agents and the benefits associated therewith.
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