Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity

This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context

Towards reactive navigation and attention skills for 3D intelligent characters

This paper presents a neural design which is able to provide the necessary reactive navigation and attention skills for 3D embodied agents (virtual humanoids or characters). Based on Grossberg’s neural model of conditioning [6], as recently implemented by Chang and Gaudiando [7], and according to the Adaptative Resonance Theory (ART) and the neuroscientific concepts associated, the neural design introduced has been divided in two main phases. Firstly, an environmentcategorization phase, where an on-line pattern recognition and categorization of the current agent sensory input data is carried out by a self organizing neural network, which will finally provide the agent’s short term memory layer(STM). Secondly, and based on the classical conditioning paradigm, the model will associate the interesting STM states, from the navigation or attention points of view, to finally simulate these necessary skills for 3D characters or humanoids. Finally, we will show some experimental navigational results, through the integration of the model presented in 3D virtual environments.Partially supported by the GVA-project CTIDIB-2002-182 (Spain)

Path finding on a spherical self-organizing map using distance transformations

Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible to use these maps to track temporal and state changes within a high-dimensional space. A popular dimensionality reduction method that produces a mapping for these purposes is the Self-Organizing Map. By using its topology preserving capabilities with a colour-based visualization method known as the U-Matrix, state transitions can be visualized as trajectories on the resulting mapping. Through these trajectories, one can gather information on the transition path between two points in the original high-dimensional state space. This raises the interesting question of whether or not the Self-Organizing Map can be used to discover the transition path between two points in an n-dimensional space. In this thesis, we use a spherically structured Self-Organizing Map called the Geodesic Self-Organizing Map for dimensionality reduction and the creation of a topological mapping that approximates the n-dimensional space. We rst present an intuitive method for a user to navigate the surface of the Geodesic SOM. A new application of the distance transformation algorithm is then proposed to compute the path between two points on the surface of the SOM, which corresponds to two points in the data space. Discussions will then follow on how this application could be improved using some form of surface shape analysis. The new approach presented in this thesis would then be evaluated by analyzing the results of using the Geodesic SOM for manifold embedding and by carrying out data analyses using carbon dioxide emissions data

Path finding on a spherical self-organizing map using distance transformations

Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible to use these maps to track temporal and state changes within a high-dimensional space. A popular dimensionality reduction method that produces a mapping for these purposes is the Self-Organizing Map. By using its topology preserving capabilities with a colour-based visualization method known as the U-Matrix, state transitions can be visualized as trajectories on the resulting mapping. Through these trajectories, one can gather information on the transition path between two points in the original high-dimensional state space. This raises the interesting question of whether or not the Self-Organizing Map can be used to discover the transition path between two points in an n-dimensional space. In this thesis, we use a spherically structured Self-Organizing Map called the Geodesic Self-Organizing Map for dimensionality reduction and the creation of a topological mapping that approximates the n-dimensional space. We rst present an intuitive method for a user to navigate the surface of the Geodesic SOM. A new application of the distance transformation algorithm is then proposed to compute the path between two points on the surface of the SOM, which corresponds to two points in the data space. Discussions will then follow on how this application could be improved using some form of surface shape analysis. The new approach presented in this thesis would then be evaluated by analyzing the results of using the Geodesic SOM for manifold embedding and by carrying out data analyses using carbon dioxide emissions data

A Pedagogy for Original Synners

Part of the Volume on Digital Young, Innovation, and the UnexpectedThis essay begins by speculating about the learning environment of the class of 2020. It takes place entirely in a virtual world, populated by simulated avatars, managed through the pedagogy of gaming. Based on this projected version of a future-now-in-formation, the authors consider the implications of the current paradigm shift that is happening at the edges of institutions of higher education. From the development of programs in multimedia literacy to the focus on the creation of hybrid learning spaces (that combine the use of virtual worlds, social networking applications, and classroom activities), the scene of learning as well as the subjects of education are changing. The figure of the Original Synner is a projection of the student-of-the-future whose foundational literacy is grounded in their ability to synthesize information from multiple information streams

Community Networking as Radical Practice

The practitioners of community networking sense that its nature as a Movement is ending. But what does that mean? As daily life online becomes ordinary, and as capacity to engender community online becomes an ordinary function of daily life, what is effective in advancing community networking as a community of practice? How can we get a clear view of community networking as radical practice in social change? Finding answers to these questions requires, in part, defining the roles of technologies and individuals in the structuring of social networks in unexpected ways. Effective community networking then occurs when individuals act cooperatively to make the Internet "happen" more rapidly in support of the need for community and the need for change in local governance