Supporting Agent-Oriented Software Engineering for Data Mining Enhanced Agent Development

  • Andreas L. Symeonidis
  • Panagiotis Toulis
  • Pericles A. Mitkas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7607)


The emergence of Multi-Agent systems as a software paradigm that most suitably fits all types of problems and architectures is already experiencing significant revisions. A more consistent approach on agent programming, and the adoption of Software Engineering standards has indicated the pros and cons of Agent Technology and has limited the scope of the, once considered, programming ‘panacea’. Nowadays, the most active area of agent development is by far that of intelligent agent systems, where learning, adaptation, and knowledge extraction are at the core of the related research effort. Discussing knowledge extraction, data mining, once infamous for its application on bank processing and intelligence agencies, has become an unmatched enabling technology for intelligent systems. Naturally enough, a fruitful synergy of the aforementioned technologies has already been proposed that would combine the benefits of both worlds and would offer computer scientists with new tools in their effort to build more sophisticated software systems. Current work discusses Agent Academy, an agent toolkit that supports: a) rapid agent application development and, b) dynamic incorporation of knowledge extracted by the use of data mining techniques into agent behaviors in an as much untroubled manner as possible.


Data Mining Multiagent System Code Block Agent Behavior Data Mining Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adriaans, P., Zantinge, D.: Data Mining. Addison-Wesley, Reading (1996)Google Scholar
  2. 2.
    Bellifemine, F., Poggi, A., Rimassa, G.: Developing Multi-agent Systems with JADE. In: Castelfranchi, C., Lespérance, Y. (eds.) ATAL 2000. LNCS (LNAI), vol. 1986, pp. 89–101. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Cao, L.: Data mining and multi-agent integration. Springer-Verlag New York Inc. (2009)Google Scholar
  4. 4.
    Cao, L., Weiss, G., Yu, P.S.: A brief introduction to agent mining. In: Autonomous Agents and Multi-Agent Systems (2012)Google Scholar
  5. 5.
    Chen, M.-S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. on Knowledge and Data Engineering 8, 866–883 (1996)CrossRefGoogle Scholar
  6. 6.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: Knowledge discovery and data mining: Towards a unifying framework. In: Knowledge Discovery and Data Mining, pp. 82–88 (1996)Google Scholar
  7. 7.
    Foster, I., Jennings, N.R., Kesselman, C.: Brain meets brawn: why grid and agents need each other. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004, pp. 8–15 (2004)Google Scholar
  8. 8.
    Gimpel, H., Jennings, N.R., Kersten, G., Okenfels, A., Weinhardt, C.: Negotiation, auctions and market engineering. Springer (2008)Google Scholar
  9. 9.
    Gruber, T.: Collective knowledge systems: Where the social web meets the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web 6(1), 4–13 (2008); Semantic Web and Web 2.0CrossRefGoogle Scholar
  10. 10.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers (2001)Google Scholar
  11. 11.
    McCann, J.A., Huebscher, M.C.: Evaluation Issues in Autonomic Computing. In: Jin, H., Pan, Y., Xiao, N., Sun, J. (eds.) GCC 2004. LNCS, vol. 3252, pp. 597–608. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Kitchenham, B.A.: Evaluating software engineering methods and tool, part 2: selecting an appropriate evaluation method technical criteria. SIGSOFT Softw. Eng. Notes 21(2), 11–15 (1996)CrossRefGoogle Scholar
  13. 13.
    Maimon, O., Rokach, L. (eds.): Soft Computing for Knowledge Discovery and Data Mining. Springer (2008)Google Scholar
  14. 14.
    Symeonidis, A.L., Mitkas, P.A.: Agent Intelligence Through Data Mining. Springer Science and Business Media (2005)Google Scholar
  15. 15.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andreas L. Symeonidis
    • 1
    • 2
  • Panagiotis Toulis
    • 3
  • Pericles A. Mitkas
    • 1
    • 2
  1. 1.Electrical & Computer Engineering DepartmentAristotle University of ThessalonikiGreece
  2. 2.Informatics and Telematics InstituteCERTHThessalonikiGreece
  3. 3.Department of StatisticsHarvard UniversityBostonUSA

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