Video Game Navigation: A Classification System for Navigational Acts

Navigation in video games has been a vastly neglected topic in game studies. In this paper a classification system for navigational acts has been developed through theoretical work as well as the analysis of multiple games. The result is an exclusive five-step classification system. Moreover, the development showed that navigational acts are highly dependent on the environment in which they occur. The system is a first step towards a deeper understanding of how the player navigates the gameworld, instead of what she navigates

Genus Computing for 3D digital objects: algorithm and implementation

This paper deals with computing topological invariants such as connected components, boundary surface genus, and homology groups. For each input data set, we have designed or implemented algorithms to calculate connected components, boundary surfaces and their genus, and homology groups. Due to the fact that genus calculation dominates the entire task for 3D object in 3D space, in this paper, we mainly discuss the calculation of the genus. The new algorithms designed in this paper will perform: (1) pathological cases detection and deletion, (2) raster space to point space (dual space) transformation, (3) the linear time algorithm for boundary point classification, and (4) genus calculation.Comment: 12 pages 7 figures. In Proceedings of the Workshop on Computational Topology in image context 2009, Aug. 26-28, Austria, Edited by W. Kropatsch, H. M. Abril and A. Ion, 200

Topological exploration of artificial neuronal network dynamics

One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechanics to describe the spatiotemporal structure of such network dynamics. Our novel approach employs tools from algebraic topology to characterize the global properties of network structure and dynamics. We propose a method based on persistent homology to automatically classify network dynamics using topological features of spaces built from various spike-train distances. We investigate the efficacy of our method by simulating activity in three small artificial neural networks with different sets of parameters, giving rise to dynamics that can be classified into four regimes. We then compute three measures of spike train similarity and use persistent homology to extract topological features that are fundamentally different from those used in traditional methods. Our results show that a machine learning classifier trained on these features can accurately predict the regime of the network it was trained on and also generalize to other networks that were not presented during training. Moreover, we demonstrate that using features extracted from multiple spike-train distances systematically improves the performance of our method