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|Appears in Collections:||Computing Science and Mathematics eTheses|
|Title: ||Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems|
|Author(s): ||Niazi, Muaz A K|
|Supervisor(s): ||Hussain, Amir|
|Keywords: ||Complex adaptive systems|
|Issue Date: ||30-Jun-2011|
|Publisher: ||University of Stirling|
|Citation: ||Niazi MA, Hussain A. Social Network Analysis of trends in the consumer electron- ics domain. In: Proc Consumer Electronics (ICCE), 2011 IEEE International Conference on, Las Vegas, NV, 9-12 Jan. 2011, 2011. pp 219-220.|
Niazi MA, Hussain A, Baig AR, Bhatti S. Simulation of the research process. 40th Conference on Winter Simulation. Miami, FL: Winter Simulation Conference; 2008. pp 1326-1334.
Niazi MA, Hussain A, Kolberg M. Verification &Validation of Agent Based Simu- lations using the VOMAS (Virtual Overlay Multi-agent System) approach. MAS&S 09 at Multi-Agent Logics, Languages, and Organisations Federated Workshops. Torino, Italy; 2009. pp 1-7
Niazi MA, Siddique Q, Hussain A, Kolberg M. Verification and Validation of an Agent-Based Forest Fire Simulation Model. SCS Spring Simulation Conference. Orlando, FL, USA: ACM; 2010. pp 142-149
Niazi MA, Hussain A. Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey. Springer Scientometrics 2011;In- press.
Niazi MA, Hussain A. Agent based Tools for Modeling and Simulation of Self- Organization in Peer-to-Peer, Ad-Hoc and other Complex Networks. IEEE Com- munications Magazine 2009; 47(3):163 - 173.
Niazi MA, Hussain A. A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments. IEEE Sensors Journal 2011; 11(2):404-412
Niazi MA, Hussain A. Sensing Emergence in Complex Systems. IEEE Sensors Journal 2011; 11(10): 2479-2480
|Abstract: ||Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a
variety of domain-specific approaches and applications. However, while cas researchers
are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains.
In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers
to adopt a suitable framework level on the basis of available data types, their research
study objectives and expected outcomes, thus allowing them to better plan and conduct
their respective research case studies.
Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of
cas components is available, with the aim of detecting emergent patterns in the cas under
study. The exploratory agent-based modeling level of the proposed framework allows for
the development of proof-of-concept models for the cas system, primarily for purposes of
exploring feasibility of further research. Descriptive agent-based modeling level of the
proposed framework allows for the use of a formal step-by-step approach for developing
agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves.|
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