A short survey on modern virtual environments that utilize AI and synthetic data

Within a rather abstract computational framework Artificial Intelligence (AI) may be defined as intelligence exhibited by machines. In computer science, though, the field of AI research defines itself as the study of “intelligent agents.” In this context, interaction with popular virtual environments, as for instance in virtual game playing, has gained a lot of focus recently in the sense that it provides innovative aspects of AI perception that did not occur to researchers until now. Such aspects are typically formed by the computational intelligent behavior captured through interaction with the virtual environment, as well as the study of graphic models and biologically inspired learning techniques, like, for instance, evolutionary computation, neural networks, and reinforcement learning. In this short survey paper, we attempt to provide an overview of the most recent research works on such novel, yet quite interesting, research domains. We feel that this topic forms an attractive candidate for fellow researchers that came into sight over the last years. Thus, we initiate our study by presenting a brief overview of our motivation and continue with some basic information on recent virtual graphic models utilization and the state-of-the-art on virtual environments, which constitutes two clearly identifiable components of the herein attempted summarization. We then continue, by briefly reviewing the interesting video games territory, and by discerning and discriminating its useful types, thus envisioning possible further utilization scenarios for the collected information. A short discussion on the identified trends and a couple of future research directions conclude the paper

A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics

Living in the “era of social networking”, we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this “socially-generated knowledge” (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely location-based information included within geo-tagged photos that we collect from Flickr, along with plain simple textual information from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into “tile”-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results