  The research activity outlined in this PhD thesis is devoted to define a
general approach, a concrete architecture and a prototype Process Management
System (PMS) for the automated adaptation of dynamic processes at run-time, on
the basis of a declarative specification of process tasks and relying on
well-established reasoning about actions and planning techniques. The purpose
is to demonstrate that the combination of procedural and imperative models with
declarative elements, along with the exploitation of techniques from the field
of artificial intelligence (AI), such as Situation Calculus, IndiGolog and
automated planning, can increase the ability of existing PMSs of supporting
dynamic processes. To this end, a prototype PMS named SmartPM, which is
specifically tailored for supporting collaborative work of process participants
during pervasive scenarios, has been developed. The adaptation mechanism
deployed on SmartPM is based on execution monitoring for detecting failures at
run-time, which does not require the definition of the adaptation strategy in
the process itself (as most of the current approaches do), and on automatic
planning techniques for the synthesis of the recovery procedure.
