Using string-matching to analyze hypertext navigation

A method of using string-matching to analyze hypertext navigation was developed, and evaluated using two weeks of website logfile data. The method is divided into phases that use: (i) exact string-matching to calculate subsequences of links that were repeated in different navigation sessions (common trails through the website), and then (ii) inexact matching to find other similar sessions (a community of users with a similar interest). The evaluation showed how subsequences could be used to understand the information pathways users chose to follow within a website, and that exact and inexact matching provided complementary ways of identifying information that may have been of interest to a whole community of users, but which was only found by a minority. This illustrates how string-matching could be used to improve the structure of hypertext collections

Generating trails automatically, to aid navigation when you revisit an environment

A new method for generating trails from a person’s movement through a virtual environment (VE) is described. The method is entirely automatic (no user input is needed), and uses string-matching to identify similar sequences of movement and derive the person’s primary trail. The method was evaluated in a virtual building, and generated trails that substantially reduced the distance participants traveled when they searched for target objects in the building 5-8 weeks after a set of familiarization sessions. Only a modest amount of data (typically five traversals of the building) was required to generate trails that were both effective and stable, and the method was not affected by the order in which objects were visited. The trail generation method models an environment as a graph and, therefore, may be applied to aiding navigation in the real world and information spaces, as well as VEs

The benefits of using a walking interface to navigate virtual environments

Navigation is the most common interactive task performed in three-dimensional virtual environments (VEs), but it is also a task that users often find difficult. We investigated how body-based information about the translational and rotational components of movement helped participants to perform a navigational search task (finding targets hidden inside boxes in a room-sized space). When participants physically walked around the VE while viewing it on a head-mounted display (HMD), they then performed 90% of trials perfectly, comparable to participants who had performed an equivalent task in the real world during a previous study. By contrast, participants performed less than 50% of trials perfectly if they used a tethered HMD (move by physically turning but pressing a button to translate) or a desktop display (no body-based information). This is the most complex navigational task in which a real-world level of performance has been achieved in a VE. Behavioral data indicates that both translational and rotational body-based information are required to accurately update one's position during navigation, and participants who walked tended to avoid obstacles, even though collision detection was not implemented and feedback not provided. A walking interface would bring immediate benefits to a number of VE applications

Three levels of metric for evaluating wayfinding

Three levels of virtual environment (VE) metric are proposed, based on: (1) users’ task performance (time taken, distance traveled and number of errors made), (2) physical behavior (locomotion, looking around, and time and error classification), and (3) decision making (i.e., cognitive) rationale (think aloud, interview and questionnaire). Examples of the use of these metrics are drawn from a detailed review of research into VE wayfinding. A case study from research into the fidelity that is required for efficient VE wayfinding is presented, showing the unsuitability in some circumstances of common metrics of task performance such as time and distance, and the benefits to be gained by making fine-grained analyses of users’ behavior. Taken as a whole, the article highlights the range of techniques that have been successfully used to evaluate wayfinding and explains in detail how some of these techniques may be applied

Human and Machine Learning in Non-Markovian Decision Making

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next state depends on only the current state and action. Little is known about non-Markovian decision making, where the next state depends on more than the current state and action. Learning is non-Markovian, for example, when there is no unique mapping between actions and feedback. We have produced a model based on spiking neurons that can handle these non-Markovian conditions by performing policy gradient descent. Here, we examine the model’s performance and compare it with human learning and a Bayes optimal reference, which provides an upper-bound on performance. We find that in all cases, our population of spiking neurons model well-describes human performance

Guiding Techniques for Collaborative Exploration in Multi-Scale Shared Virtual Environments

International audienceExploration of large-scale 3D Virtual Environments (VEs) is difficult because of lack of familiarity with complex virtual worlds, lack of spatial information that can be offered to users and lack of sensory (visual, auditory, locomotive) details compared to exploration of real environments. To address this problem, we present a set of metaphors for assisting users in collaborative navigation to perform common exploration tasks in shared collaborative virtual environments. Our propositions consist in three guiding techniques in the form of navigation aids to enable one or several users (called helping user(s)) to help one main user (called exploring user) to explore the VE efficiently. These three techniques consist in drawing directional arrows, lighting up path to follow, and orienting a compass to show a direction to the exploring user. All the three techniques are generic so they can be used for any kind of 3D VE, and they do not affect the main structure of the VE so its integrity is guaranteed. To compare the efficiency of these three guiding techniques, we have conducted an experimental study of a collaborative task whose aim was to find hidden target objects in a complex and multi-scale shared 3D VE. Our results show that although the directional arrows and compass surpassed the light source for the navigation task, these three techniques are completely appropriate for guiding a user in 3D complex VEs

Influence of Motivation on Wayfinding

This research explores the role of affect in the domain of human wayfinding by asking if increased motivation will alter the performance across various routes of increasing complexity. Participants were asked to perform certain navigation tasks within an indoor Virtual Reality (VR) environment under either motivated and not-motivated instructions. After being taught to navigate along simple and complex routes, participants were tested on both the previously learned routes and new routes that could be implicitly derived from the prior spatial knowledge. Finally, participants were tested on their ability to follow schematized instructions to explore familiar and unfamiliar areas in the VR environment. Performance of the various spatial tasks across the motivated and control groups indicated that motivation improved performance in all but the most complex conditions. Results of the empirical study were used to create a theoretical model that accounts for the influence of affect on the access of route knowledge. Results of the research suggest the importance of including past knowledge and affect of the traveler as components of future wayfinding systems

The effect of trails on first-time and subsequent navigation in a virtual environment

Trails are a little-researched type of aid that offers great potential benefits for navigation, especially in virtual environments (VEs). An experiment was performed in which participants repeatedly searched a virtual building for target objects assisted by: (1) a trail, (2) landmarks, (3) a trail and landmarks, or (4) neither. The trail was displayed as a white line that showed exactly where a participant had` previously traveled. The trail halved the distance that participants traveled during first-time searches, indicating the immediate benefit to users if even a crude form of trail were implemented in a variety of VE applications. However, the general clutter or “pollution” produced by trails reduced the benefit during subsequent navigation and, in the later stages of these searches, caused participants to travel more than twice as far as they needed to, often accidentally bypassing targets even when a trail led directly to them. The proposed solution is to use gene alignment techniques to extract a participant’s primary trail from the overall, polluted trail, and graphically emphasize the primary trail to aid navigation

Using trails to improve map generation for virtual agents in large scale, online environments

This thesis looks at improving the generation of maps for intelligent virtual agents in large scale environments. Virtual environments are growing larger in size and becoming more complex. There is a major challenge in providing agents that are able to autonomously generate their own map representations of the environment for use in navigation. Currently, map generation for agents in large scale virtual environments is performed either by hand or requires a lengthy pre-processing step where the map is built online. We are interested in environments where this process is not possible, such as those that encourage user generated content. We look at improving map generation in these environments by using trails. Trails are a set of observations of how a user navigates an environment over time. By observing trails an agent is able to identify free space in an environment and how to navigate between points without needing to perform any collision checking. We found that trails in a virtual environments are a useful source of information for an agent's map building process. Trails can be used to improve rapidly exploring randomised tree and probabilistic roadmap generation, as well as being used as a source of information for segmenting maps in very large scale environments