SmartPM: Automatic Adaptation of Dynamic Processes at Run-Time

TL;DR

This paper presents SmartPM, a novel process management system that automatically adapts dynamic processes at run-time using AI techniques like planning and reasoning, enhancing flexibility and robustness in collaborative scenarios.

Contribution

It introduces a general approach and prototype system that combines procedural, imperative, and declarative models with AI reasoning for automated process adaptation at run-time.

Findings

SmartPM effectively detects process failures during execution.

Automated planning generates recovery procedures without pre-defined strategies.

The system supports collaborative work in pervasive environments.

Abstract

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.