A main objective of this work aims to improve the understanding of how people explore the space or perform a naive search within indoor VEs. Given the significance of this issue, the analysis of user spatial behaviour, as reflected in the movement paths received considerable attention.
This analysis had a twofold purpose. Firstly, it seeks to extract navigational pat- terns in terms of rules or strategies underpinning the trajectory paths. As highlighted in Section 3.2.4, addressing such implicit knowledge challenges the traditional techniques of knowledge elicitation. Because of their sensitivity to learning temporal sequences — navigation is after all a spatio-temporal process — recurrent neural networks are partic- ularly suitable for implicitly capturing such rules (Elman, 1990; Ellis and Humphreys, 1999; Ghiselli-Crippa, 2000). The richness and accuracy of the quantitative data record- ing users’ spatial behaviours support this novel methodological approach.
Secondly, designing an adaptive VE for supporting navigation implies ensuring its sensitivity to different types of users. Such adaptive VEs should have the ability to discriminate between different groups of users, groups which should differ not only in their performance on spatial tasks, for which navigation support is primarily provided, but also in their spatial behaviour. The latter aspect, a reflection of different spatial search styles, strategies and rules, represents a basis for identifying these groups. As presented in Section 8.2, Self-Organising Maps (SOM) have been successfully employed for trajectory classification in the area of visual surveillance (Grimson et al., 1998; Owens and Hunter, 2000). The analysis of spatial behaviour which has been proposed within this thesis offers two major benefits:
• Despite the increasing usage of machine learning techniques in the area of user modelling (Kobsa, 1994), to the best of my knowledge, the potential of Artificial Neural Networks (ANNs) for analysing user spatial behaviour in VEs has never been harnessed. Thus, this research advocates for the adoption of ANN techniques for spatial behaviour analysis, aspect which constitutes a significant methodolog- ical contribution of this thesis. A detailed description of the machine learning techniques which have been employed for the work carried out within this thesis is offered in Chapter 6.
• In the attempt to increase the validity of study findings, a second approach in analysing user spatial behaviour has been employed. Apart from reinforcing some previous findings, such an additional approach would highlight new aspects, which were not captured by the initial, machine learning approach. Searching for al- ternative ways to analyse trajectory paths, my attention was captured by their geometric features. Comparing movement paths with some prototypical curves, would offer a considerable advantage. The equation of such curves could be used to predict motion trajectory, and therefore it could offer a valuable support for navigation assistance.
Thus, the analysis of spatial behaviour has been also performed by an approach inspired by the geometry of curve. As described in Chapter 9, B´ezier curves have been successfully applied to robot navigation (Arakawa et al., 1995; Khatib et al., 1997), or avatar navigation in VEs (Chung and Hahn, 1999; Pettre et al., 2002; Boulic et al., 1994). Applying these curves for modelling trajectories followed by users, while they navigate within a VE, is another methodological contribution of this thesis.
The use of B´ezier curves represents a complementary approach to the machine learning approach. It provides a basis for the diagnosis of navigational patterns and for discriminating different groups of users.
5.10
Summary
This research constitutes a case study for which the results are merely preliminary. Study limitations consist of the non-random sampling procedure for selecting partic- ipants and of relatively small sample size. These are primarily due to the limited resources of this study. The large distribution of study participants along the differ- ent dimensions of the investigated variables (e.g., personality cognitive style) is partly explained by these limitations. This probably accounts for the lack of statistical signif- icance for some of study results. Despite the relatively small number of participants in the study, the amount of data recorded during their interaction with the VE is impres- sive.
Nevertheless, the above limitations reduce the generality of the obtained findings. However, the strength of study outcomes does not reside in their generalisation power but rather in their exploratory potential of identifying questions, selecting measure- ment constructs, developing measures and methodologies (Lynn, 1991). Further studies should be carried out in order to replicate these findings.
This chapter presented a thorough description of study methodology. It connects the theoretical chapters which have been previously described (Chapters 2, 3 and 4), with the results chapters which will follow (Chapters 7, 8, 9 and 10). The study apparatus, procedure, methods, and instruments were presented in detail. Given the significance of analysing spatial behaviour, two complementary approaches have been proposed. One involves the use of various machine learning techniques (Chapter 8), while the other one attempts to model trajectory with B´ezier curves (Chapter 9). The following methodological chapter offers a reviewof the machine learning techniques used within this thesis.
Chapter 6
Artificial Intelligence for
Advanced Data Interpretation
and Exploitation
Chapter 1 Introduction Background Chapters Chapter 2 Usability of VE Chapter 3 Navigation in VE Chapter 4 Sense of Presence Methodological Chapters Chapter 6Artificial Intelligence for Advanced Data Interpretation and Exploitation Chapter 5 Methodology Chapter 12 Conclusions Results Chapters Chapter 8
Individual Differences in Navigational Patterns Machine Learning Approach
Chapter 9
Individual Differences in Navigational Patterns Geometry of Curve Approach
Chapter 10
Individual Differences in Experiencing Presence Chapter 7
Individual Differences related to Usability
Chapter 11 Towards Accommodating Individual Differences: Design Guidelines
6.1
Introduction
There are two important objectives of this thesis (see Section 5.9), supported by different machine learning techniques and intelligent agents introduced in this chapter:
• Understanding howpeople explore an indoor unfamiliar VE (see Chapter 1). Thus, the analysis of user spatial behaviour received considerable attention. Such an analysis aims to identify the navigational patterns which appear in users’ trajec- tories, and to capture strategies and rules hidden in such patterns. Traditional techniques for knowledge elicitation present a series of limitations, particularly when it comes to extract implicit knowledge, inherently associated with navi- gational rules or strategies (see Section 3.2.4). The potential of connectionism models, and in particular of Recurrent Neural Networks (RNNs) offers an alter- native approach for extracting navigational rules (see Section 3.2.4). Clustering RNN best predictions has been performed by Self-Organising Maps (SOM). In order to enable symbolic rule induction, this machine learning technique has been employed together with rule induction algorithm, (i.e. C5.0), based on the ID3 algorithm.
• Designing an adaptive VE for supporting navigation of lowspatial users (see Chap- ter 1). The requirements of an adaptive system to accommodate users’ needs (see Section 2.6.1) imply that it is capable of tailoring itself in accord to these needs. Such adaptivity is not global, and its greatest potential resides rather in sys- tem’s capacity to address specifically the different needs of different groups of users. Therefore, an important characteristic of an adaptive system would be the capacity to identify the groups of users which require different adaptations. Such groups should differ both in their performance, reason to provide navigation support, but also in their navigational behaviour (see Section 5.9). The naviga- tion behaviour, an expression of different spatial strategies and rules allows an online and unobtrusive identification of these user groups. Trajectory classifica- tion presents an appealing potential for performing such kind of identification. The use of SOM provided encouraging results for trajectory classification in the area of visual surveillance (Grimson et al., 1998; Owens and Hunter, 2000) (see Section 8.2). Within this thesis, SOM was used together with Learning Vector Quantisation (LVQ) (see Section 6.4.4).
The adaptive version of ECHOES system is designed through the use of intelligent agents, used to implement the identified efficient navigational rules and strategies for assisting lowspatial users. The choice of designing a multi-agents adaptive ECHOES VE is motivated by the agent-based architecture supporting the non- adaptive version of ECHOES VE (see Section 11.4.6).
This chapter offers a reviewof different Artificial Intelligence (AI) techniques for advanced data interpretation and exploitation, which have been used within this the- sis. These techniques are organised in two main classes: machine learning techniques
and intelligent agents. The presentation of machine learning techniques is organised according to two fundamental classes of machine learning algorithms: supervised and unsupervised learning. Intelligent agents are introduced in the context of the Agent Fac- tory, an agent developing environment. The benefit of these techniques for designing adaptive VEs is further discussed.