95 research outputs found
Multi-agent evolutionary systems for the generation of complex virtual worlds
Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery
Mining developer communication data streams
This paper explores the concepts of modelling a software development project
as a process that results in the creation of a continuous stream of data. In
terms of the Jazz repository used in this research, one aspect of that stream
of data would be developer communication. Such data can be used to create an
evolving social network characterized by a range of metrics. This paper
presents the application of data stream mining techniques to identify the most
useful metrics for predicting build outcomes. Results are presented from
applying the Hoeffding Tree classification method used in conjunction with the
Adaptive Sliding Window (ADWIN) method for detecting concept drift. The results
indicate that only a small number of the available metrics considered have any
significance for predicting the outcome of a build
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