Speaker: Andrea Marrella
Title: Run-time adaptation of knowledge-intensive processes through AI techniques:
Research challenges and some solutions.
A knowledge-intensive process is one in which the people performing such process is involved in a fair degree of uncertainty. This is due to the high number of tasks to be represented and to their unpredictable nature, or to a
difficulty to model the whole knowledge of the domain of interest at design time. Typically, a knowledge-intensive process can not be modeled sufficiently by classical, static process models and workflows, because as the knowledge-intensive process proceeds, the sequence of tasks depends so much upon the specifics of the context. An important aspect of knowledge-intensive processes is that they constantly evolve and often it is unpredictable the way in how they unfold. To deal with exceptions and uncertainly introduced by such processes, the need for flexible and easy adaptable Process Management systems (aka, PMSs) has been recognized as one
of the critical success factors for any PMS.
The main focus of this presentation is to discuss about how a modeling approach towards a declarative specification of process tasks, i.e., comprising the specification of input/output artefacts and task preconditions and effects, allows to layer planning techniques on top of traditional PMSs, in order to enable run-time process adaptation, which can be done without defining explicitly any recovery policy. A prototype implementation based on the above theory has been developed through the IndiGolog execution framework.
Andrea Marrella is a PhD student in the Department of Systems and Computer Science and Engineering at Sapienza - Universitá di Roma. His research interests include business process management, automatic adaptation in process management systems, planning techniques, knowledge representation and reasoning.
More information on
can be found at http://www.dis.uniroma1.it/~marrella