A hybrid model for capturing implicit spatial knowledge

ABSTRACT

This paper proposes a machine learning-based approach for capturing rules embedded in users' movement paths while navigating in Virtual Environments (VEs). It is argued that this methodology and the set of navigational rules which it provides should be regarded as a starting point for designing adaptive VEs able to provide navigation support. This is a major contribution of this work, given that the up-to-date adaptivity for navigable VEs has been primarily delivered through the manipulation of navigational cues with little reference to the user model of navigation.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems
          Subjects: Artificial, augmented, and virtual realities

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition; Connectionism and neural nets

General Terms:
Human Factors

Keywords:
connectionism, implicit knowledge elicitation, user modelling, virtual reality