Soft temporal constraints problems allow for a natural description of scenarios where events happen over time and preferences
are associated with event distances and durations. However, sometimes such
local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the
problem, and then to
learn from them suitable preferences over distances and durations.
In this paper, we describe our learning algorithm and we show its behaviour on classes of randomly generated problems. Moreover,
we also describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal
problems, and we give experimental results to compare them.