Predicting User Actions Using Interface Agents with Individual User Models

  • Jung-Jin Lee
  • Robert McCartney
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1733)


The incompleteness and uncertainty about the state of the world and about the consequences of actions are unavoidable. If we want to predict the performance of multiuser computing systems, we have the uncertainty of what the users are going to do, and how that affects system performance. Intelligent interface agent development is one way to mitigate the uncertainty about user behaviors by predicting what users will do based on learned users’ behaviors, preferences, and intentions. This work focuses on developing user models that can analyze and predict user behavior in multi-agent systems. We have developed a formal theory of user behavior prediction based on hidden Markov models. This work learns the user model through a time-series action analysis and abstraction by taking users’ preferences and intentions into account in order to formally define user modeling.


Prediction Accuracy Hide Markov Model User Model User Behavior State Transition Probability 
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 1999

Authors and Affiliations

  • Jung-Jin Lee
    • 1
  • Robert McCartney
    • 2
  1. 1.Department of Computer ScienceUniversity of Hartford West HartfordUSA
  2. 2.Department of Computer Science and EngineeringUniversity of Connecticut StorrsUSA

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