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This paper looks at how personal conventions are unintentionally carried from the real world into virtual environments. We look at a simple example where we investigate whether avatars will follow virtual paths, or will walk on the grass. By default, people use the paths in real world parks, but we have showed that this behaviour has carried over into virtual parks. We investigated this further, postulating that the more exposure an individual had to virtual worlds the more likely they were to break with this social convention and walk on the grass. We observed the movements of agents in a virtual park on two extended occasions, one in 2010 and the other in 2012. From this we were able to see that people, in general, were still keeping to the paths except when invited to move onto the grass. We also look at the likelihood of individuals using another mode of transport, flying. Finally, we conclude that while some patterns can be seen between the 'age' of the avatar and their movements on or off the path, more investigation must be done.

Virtual worlds present a challenge for intelligent mobile agents. They are required to generate maps of very large scale, dynamic and unstructured environments in a short amount of time. We investigate how to represent maps of ever growing virtual environments, how the agent can build, update and use these maps to navigate between points in the environment. We look at trails, the movement of other people and agents in the environment as a new information source. We can use trails to improve the generation of probabilistic roadmaps in these environments and enable the agent to segment space intelligently. Our future plans are to extend this to look at dynamic environments, where the agent will have to recognise change and update the map and how this will affect the map representation.

This paper looks at the challenges and solutions for intelligent mobile agents existing in virtual environments. Representing and using a map of these very large-scale, dynamic environments is a key challenge in providing an autonomous agent for large, online worlds. We look at a method for improving the generation of probabilistic roadmaps by observing and using the movements of other avatars in an environment. We also look at methods for segmenting and scaling the map in very large environments. Finally we extend this to look at generating and updating maps in dynamic environments.

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