Non-parametric Bayes to infer playing strategies adopted in a population of mobile gamers

Virtanen, S., Rost, M., Higgs, M., Morrison, A., Chalmers, M. and Girolami, M. (2015) Non-parametric Bayes to infer playing strategies adopted in a population of mobile gamers. Stat, 54(1), pp. 46-58. (doi:10.1002/sta4.75)

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Abstract

Analysis of trace logging data collections of interactions of a heterogenous and diverse population of consumers of digital software with mobile devices provides unprecedented possibilities for understanding how software is actually used and for finding recurring patterns of software usage over the population that are exhibited to a greater or lesser degree in each individual software user. In this work, we consider an elementary mobile game played by a population of mobile gamers and collect pieces of game sessions over an extended period, resulting in a collection of users’ trace logs for multiple sessions. We develop a simple, yet flexible, non-parametric Bayes approach to infer playing strategies adopted in the population from the logged traces of game interactions. We demonstrate that our approach finds interpretable strategies and provides good predictive performance compared with alternative modelling assumptions using a non-parametric Bayes framework.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Chalmers, Professor Matthew and Higgs, Dr Matthew and Girolami, Prof Mark and Rost, Dr Mattias and Morrison, Dr Alistair
Authors: Virtanen, S., Rost, M., Higgs, M., Morrison, A., Chalmers, M., and Girolami, M.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Stat
Publisher:Wiley
ISSN:2049-1573
ISSN (Online):2049-1573

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
566711A population approach to ubicomp system designMatthew ChalmersEngineering & Physical Sciences Research Council (EPSRC)EP/J007617/1COM - COMPUTING SCIENCE