IT Ecosystems

2010 - 2012

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Classical approaches of computer science do not scale well for todays large and complex software-intensive systems. Software systems cannot be considered in isolation, since they are connected among each other and interact massively. Instead, they are to be designed as parts of a larger IT Ecosystem. In analogy to biological ecosystems, IT Ecosystems are based on the balance between individuals (autonomy) and sets of rules (control) defining equilibria within an IT Ecosystem. Maintaining and continuously evolving IT Ecosystems requires deep understanding of this balance. The research topic IT Ecosystems cuts across several research areas, including the emergence of system functions, extending classical engineering approaches, adaptive infrastructures, control of semantic diversity, and enhanced human-environment-machine interaction. These core areas were addressed by the NTH focused Research School for IT Ecosystems, a cooperation of the Universities of Braunschweig, Clausthal, and Hannover.

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[5] N. T. Le, N. Pinkwart (2013). A Comparison between a Communication-based and a Data Mining-based Learning Approach For Agents. International Journal on Intelligent Decision Technologies, 7(3), 185--195.
[4] N. T. Le, L. Märtin, C. Mumme, N. Pinkwart (2012). Communication-free detection of resource conflicts in multi-agent-based cyber-physical systems. In Proceedings of the 6th IEEE Digital Ecosystems and Technologies Conference (DEST) (pp. 1--6). Los Alamitos, CA, IEEE Computer Society Press.
[3] N. T. Le, N. Pinkwart (2012). Strategy-Based Learning through Communication with Humans. In G. Jezic, M. Kusek, N.-T. Nguyen, R. Howlett, L. Jain, eds., Lecture Notes in Computer Science (7327) - Proceedings of the 6th International KES Conference on Agents and Multi-agent Systems - Technologies and Applications (KES-AMSTA) (pp. 54--64). Berlin, Germany, Springer Verlag.
[2] M. Huhn, J. Müller, J. Görmer, G. Homoceanu, N. T. Le, L. Märtin, C. Mumme, C. Schulz, N. Pinkwart, C. Müller-Schloer (2011). Autonomous Agents in Organized Localities Regulated by Institutions. In Proceedings of the 5th IEEE Digital Ecosystems and Technologies Conference (DEST) (pp. 54--61). Los Alamitos, CA, IEEE Computer Society Press.
[1] N. T. Le, L. Märtin, N. Pinkwart (2011). Learning Capabilities of Agents in Social Systems. In R. Alhajj, J. Joshi, M. L. Shyu, eds., Proceedings of the Workshop on Issues and Challenges in Social Computing at the 12th IEEE International Conference on Information Reuse and Integration (IRI) (pp. 539--544). Las Vegas, NV, IEEE Systems, Man, and Cybernetics Society.