Soft behaviour modelling of user communities

A soft modelling approach for describing behaviour in on-line user communities is introduced in this work.Behaviour models of individual users in dynamic virtual environments have been described in the literaturein terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automataare defined and proposed to describe multiple user behaviours and to recognise larger classes of user grouphistories, such as group histories which contain unexpected behaviours. The notion of deviation from theuser community model allows defining a soft parsing process which assesses and evaluates the dynamicbehaviour of a group of users interacting in virtual environments, such as e-learning and e-businessplatforms. The soft automaton model can describe virtually infinite sequences of actions due to multipleusers and subject to temporal constraints. Soft measures assess a form of distance of observed behavioursby evaluating the amount of temporal deviation, additional or omitted actions contained in an observedhistory as well as actions performed by unexpected users. The proposed model allows the soft recognitionof user group histories also when the observed actions only partially meet the given behaviour modelconstraints. This approach is more realistic for real-time user community support systems, concerningstandard boolean model recognition, when more than one user model is potentially available, and the extentof deviation from community behaviour models can be used as a guide to generate the system support byanticipation, projection and other known techniques. Experiments based on logs from an e-learningplatform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of theproposed model.