SmartPM: Automatic Adaptation of Dynamic Processes at Run-Time
This work addresses the challenge of managing dynamic processes in collaborative environments, offering an incremental improvement over existing systems by integrating declarative and procedural models.
The thesis tackled the problem of automating adaptation of dynamic processes at run-time by developing SmartPM, a prototype Process Management System that uses AI techniques like automated planning to handle failures without pre-defined strategies, demonstrating increased support for dynamic processes in collaborative pervasive scenarios.
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.