Paper Info
Title
SmartPM: Automatic Adaptation of Dynamic Processes at Run-Time.
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.
Year
Venue
Field
2018
arXiv: Artificial Intelligence
Architecture,Situation calculus,Software engineering,Computer science,Process management system,Artificial intelligence,Machine learning,Recovery procedure
DocType
Volume
Citations 
Journal
abs/1810.06374
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Andrea Marrella127335.71