| Summary: | Most models that explain observations in time depend on a structured state space
as a basis for their modeling. We present methods to derive such a state space
and its dynamics automatically from the observations, without any knowledge of
their meaning or source. First, we build an explicit state space from an observation
history in an off-line fashion (OFESI) by starting with the space induced by
the observations and splitting a state when new substates significantly improve the
information content of the state's action distribution. We form these new substates
by grouping fixed length histories leading up to the state. We apply our algorithm
to the user modeling case and show that we can automatically build a meaningful
stochastic dynamic model of application use. Second, we discuss prediction of
the next observation and show that an approach based on on-line implicit state
identification (ONISI) from observed history outperforms other prediction methods.
Again, we are interested in user modeling, where we can predict future user actions
better than another current algorithm. Both algorithms work without knowledge
or modification of the application in use. Third, we apply our explicit state identification
algorithm to the problem of state identification for Hidden Markov Models
(SIHMM). Taking into account both observation and transition probabilities we
learn structures for Hidden Markov Models in a few iterations. === Science, Faculty of === Computer Science, Department of === Graduate
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