Tuesday, March 23, 2010

Agent-Based Verification and Validation of Process Models

Name: Hanwen Guo

Title (PhD): Agent-Based Verification and Validation of Process Models

Abstract:
Business Process Management (BPM) is an abstraction of business activities that has the ability to model, measure and improve business processes. The significant outcome of a precise and validated process model is that it improves the business’ efficiency in a systematic fashion. Virtual Environments provide participants with an almost real sense of immersion into a 3D synthetic world, with many application domains, such as military, medical and aerospace sectors investing heavily in Virtual Environment Simulations (VES).

The accurate modelling of human resources within an Enterprise has been problematic, with previous research generally using aggregated statistical models of human behaviour. Multi-Agent Systems (MAS) are a computational model for simulating the actions and interactions of autonomous individuals with an ability to assess their effects in the system. Combining BPM and VES with MAS will present a detailed human resource model that is closer to a real version of the business processes.

This research work has much to contribute to the business education and training sector. It will help enhance an organization’s competitiveness via the following benefits. Firstly, it will allow for detailed simulation and interaction with a resource model of the business processes, bringing far more accuracy in modelling. Secondly, it will help users to plan, manage and understand operational task procedures so as to improve employee abilities and work standards.

Visualizing the control perspectives of business process models within 3D virtual environments

Name: Stephen West

Title (Masters): Visualizing the control perspectives of business process models within 3D virtual environments.

Abstract: Changes in technology, market demand, competition, and workforces are just some of the challenges facing businesses today. Business Process Management (BPM) has been widely adopted to deal with these issues employing methods, techniques, and tools to support the design, enactment, management and analysis of operational business processes. The aim of BPM systems is to support the whole lifecycle of business processes. However, BPM has lacked the vision to engage the business world at its lowest level in validating the executable workflow models that are created and used by the IT infrastructure, to identify process improvements. This research project focuses on providing visualisation approaches to assist with the validation of workflow models derived from business processes. The motivation is driven by the need to provide business stakeholders with a visually understandable representation for how a business process is enacted within the real world. Satisfying this need will see a reduction in the number of times the process model lifecycle has to be repeated before a valid or correct model can be established.

Automatic Verification of Virtual Environment Content using Artificial Agents

Name: Alfredo Nantes

Title (PhD): Automatic Verification of Virtual Environment Content using Artificial Agents

Abstract: Game environments are complex interactive systems that require extensive analysis and testing to ensure that they are at a high enough quality to be released commercially. While the game software under development is partially tested by using other ordinary software, the last build of the product needs an additional and extensive beta test carried out by people that play the game in order to establish its veracity and playability. This entails additional costs from the viewpoint of a company as it requires the hiring of beta testers.

The proposed course of research aims at investigating the development of software architectures based on Computer Vision and Machine Learning techniques that will enable the test team to improve and accelerate the beta testing process.

This work comes into being as a novel project as it represents an innovative application of Machine Learning and Computer Vision techniques in the area of video games testing that, to the best of our knowledge, have never been investigated.