Agent Assignment for Process Management: Pattern based Agent Performance Evaluation
In almost all workflow management system the role concept is determined once at the introduction of workflow application and is not reevaluated to observe how successfully certain processes are performed by the authorized agents. This paper describes an approach which evaluates how agents are working successfully and feed this information back for future agent assignment to achieve maximum business benefit for the enterprise. The approach is called Pattern based Agent Performance Evaluation (PAPE) and is based on machine learning technique combined with post processing technique. It consists of five phases: Preprocessing Phase (Dataset is prepared), Integrated Data Structure Generation Phase (Dataset is used to generate integrated data structure), Agent Performance Evaluation Phase (integrated data structure is used to evaluate agent performance), Agent Assignment Learning Phase (Agent assignment rules are learned based on performance) and Agent Assignment Updating Phase (Learned agent assignment rules are updated in WFMS to achieve future business success). We report on the result of our experiments and discuss issues and improvement of our approach.
|Conference:||2009 AAMAS Workshop on Agent and Data Mining Interaction (ADMI 2009). May 10-15, 2009 Budapest, Hungary. LNCS5680, pp. 155-169, 2009. Springer-Verlag, Berlin Heidelberg 2009|