  A common way of learning to perform a task is to observe how it is carried
out by experts. However, it is well known that for most tasks there is no
unique way to perform them. This is especially noticeable the more complex the
task is because factors such as the skill or the know-how of the expert may
well affect the way she solves the task. In addition, learning from experts
also suffers of having a small set of training examples generally coming from
several experts (since experts are usually a limited and expensive resource),
being all of them positive examples (i.e. examples that represent successful
executions of the task). Traditional machine learning techniques are not useful
in such scenarios, as they require extensive training data. Starting from very
few executions of the task presented as activity sequences, we introduce a
novel inductive approach for learning multiple models, with each one
representing an alternative strategy of performing a task. By an iterative
process based on generalisation and specialisation, we learn the underlying
patterns that capture the different styles of performing a task exhibited by
the examples. We illustrate our approach on two common activity recognition
tasks: a surgical skills training task and a cooking domain. We evaluate the
inferred models with respect to two metrics that measure how well the models
represent the examples and capture the different forms of executing a task
showed by the examples. We compare our results with the traditional process
mining approach and show that a small set of meaningful examples is enough to
obtain patterns that capture the different strategies that are followed to
solve the tasks.
