A framework for online user behavior soft modeling is presented in this work. Behavior models of users in dynamic virtual environments has been described in the literature in terms of timed transition automata which can be compiled in a planning domain. The extended notion of soft timed transition automata is proposed in order to recognize a larger class of user histories. The notion of deviation from the user model allows to assess and evaluate in real time the dynamic behavior of users acting in virtual environments, such as e-learning and e-business platforms. The timed automata model allows to describe virtually infinite sequences of user actions subject to temporal constraints, while soft measures allows to assess recognition of behaviors by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed behavior. The proposed model allow the partial recognition of user history also when the observed actions only partially meets the given behavior model constraints. This approach is more realistic for real time user support systems, with respect to standard boolean model recognition, when more than one user model is potentially available the amount of deviation from the models can be used as guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platforms and plan compilation of the soft timed automaton shows the expressivity of the proposed model.
['Alfredo Milani', 'Judith Jassó', 'Silvia Suriani']
Modeling Online User Behavior