A Framework for Uniform Development of Intelligent Virtual Agents

  • George Anastassakis
  • Themis Panayiotopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


As the field of Intelligent Virtual Agents evolves and advances, an ever increasing number of functional and useful applications are presented. Intelligent Virtual Agents have become more realistic, intelligent and sociable, with apparent and substantial benefits to domains such as training, tutoring, simulation and entertainment. However, even though many end-users can enjoy these benefits today, the development of such applications is restricted to specialized research groups and companies. Obvious and difficult-to-overcome factors contribute to this. The inherent complexity of such applications results in increased theoretical and technical requirements to their development. Furthermore, Intelligent Virtual Agent systems today typically offer ad hoc, if any, design and development means that lack completeness and a general-purpose character. Significant efforts have been successfully made towards deriving globally accepted standards; nevertheless these mostly focus on communication between heterogeneous systems and not on design and development. In this paper, we present our current efforts towards a novel architecture for Intelligent Virtual Agents which is based on our previous work in the field and encompasses the full range of characteristics considered today as fundamental to achieving believable Intelligent Virtual Agent behaviour. In the spirit of enabling and easing application design and development, as well as facilitating further research, our architecture is tightly coupled with a behaviour specification language that uniformly covers all aspects and stages of the development process. We also present the key guidelines for a minimal but functional implementation, aimed in validation and experimentation.


Scene Graph Virtual Human Action Primitive Concurrent Operation Virtual Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • George Anastassakis
    • 1
  • Themis Panayiotopoulos
    • 1
  1. 1.Department of InformaticsUniversity of PiraeusGreece

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