Data mining for agent reasoning: A synergy for training intelligent agents

Engineering Applications of Artificial Intelligence

Volumn 20, Issue 8, 2007, Pages 1097-1111

  Symeonidis, Andreas L ^a,b   Chatzidimitriou, Kyriakos C ^c   Athanasiadis, Ioannis N ^d   Mitkas, Pericles A ^a,b  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b INFORMATION TECHNOLOGIES INSTITUTE   (Greece)

^c Department of Computer Science   (United States)

^d DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE IDSIA   (Switzerland)

Author keywords

Agent reasoning; Agent technology; Agent training; Data mining; Knowledge model

Indexed keywords

DATABASE SYSTEMS; FORMAL LOGIC; INTELLIGENT AGENTS; KNOWLEDGE MANAGEMENT; PROBLEM SOLVING;

AGENT TECHNOLOGY; AGENT TRAINING; EXTRACTION OF KNOWLEDGE; LOGIC LIMITATIONS;

DATA MINING;

EID: 35548993284     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2007.02.009     Document Type: Article

References (36)

1. Adriaans P., and Zantige D. Data Mining (1996), Addison-Wesley, London
2. Agent Academy Consortium, the, 2004a. The Agent Academy Project [cited 1/09/2004]. Available from the World Wide Web 〈http://AgentAcademy.iti.gr〉.
3. Agent Academy Consortium, the, 2004b. Requirements and specifications of Agent Academy [cited 1/09/2004]. Available from the World Wide Web 〈http://AgentAcademy.iti.gr/downloads.htm〉.
4. Arthur B.W. Inductive reasoning and bounded rationality. American Economic Review 84 2 (1994) 406-411
5. Athanasiadis I.N., and Mitkas P.A. An agent-based intelligent environmental monitoring system. Management of Environmental Quality Journal 15 3 (2004) 238-249
6. Bellifemine, F., Poggi, A., Rimassa, G., 2000. Developing multi-agent systems with JADE. The Seventh International Workshop on Agent Theories, Architectures, and Languages (ATAL-2000), Boston, MA.
7. Caglayan A., Harrison C., and Harrison C.G. Agent Sourcebook: A Complete Guide to Desktop, Internet, and Intranet Agents (1997), Wiley, New York
8. Chen Z. Computational Intelligence for Decision Support (1999), CRC Press, Boca Raton, FL
9. Chen M.S., Han J., and Yu P.S. Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8 6 (1996) 866-883
10. Data Mining Group, the, 2001. Predictive Model Markup Language Specifications (PMML), ver. 2.0 [cited 1/09/2004]. Available from the World Wide web: 〈http://www.dmg.org〉.
11. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1996. Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of The Second International Conference on Knowledge Discovery and Data Mining. AAAI/MIT Press, Portland, USA, pp. 82-88.
12. Ferber J. Multi-agent Systems-An Introduction to Distributed Artificial Intelligence (1999), Addison-Wesley, London
13. Fernandes, A.A.A., 2000. Combining inductive and deductive inference in knowledge management tasks. In: Proceedings of the 11th International Workshop on Database and Expert Systems Applications (First International Workshop on Theory and Applications of Knowledge Management-TAKMA 2000). IEEE Computer Society, Silver pring, MD, pp. 1109-1114.
14. Friedman-Hill, E.J., 2003. Jess, The Expert System Shell for the Java Platform, version 6.1, CA, Sandia National Laboratories [cited 1/09/2004]. Available from the World Wide Web 〈http://herzberg.ca.sandia.gov/jess/〉.
15. Galitsky B., and Pampapathi R. Deductive and inductive reasoning for processing the claims of unsatisfied customers. In: The Proceedings of the 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE 2003) (2003), Springer, Heidelberg pp. 21-30
16. Han J., and Kamber M. Data Mining: Concepts and Techniques (2001), Morgan Kaufmann, San Diego
17. In: Jennings N.R., and Wooldridge M.J. (Eds). Agent Technology: Foundations, Applications and Markets (1998), Springer, Berlin
18. Jennings N.R., Sycara K., and Wooldridge M.J. A roadmap of agent research and development. The International Journal of Autonomous Agents and Multi-Agent Systems 1 (1998) 7-38
19. Kaburlasos, V.G., Athanasiadis, I.N., Mitkas, P.A., 2006. Fuzzy lattice reasoning (FLR) classifier and its application on ambient ozone estimation. International Journal of Approximate Reasoning, paper in press, available online August 2006.
20. Kargupta, H., Hamzaoglu, I., Stafford, B., 1997. PADMA: PArallel Data Mining Agents for scalable text classification. In: The Proceedings of High Performance Computing '97.
21. Kehagias, D., Chatzidimitriou, K., Symeonidis, A.L., Mitkas, P.A., 2004. Information agents cooperating with heterogeneous data sources for customer-order management. The 19th Annual ACM Symposium on Applied Computing (SAC 2004), Nicosia, Cyprus, pp. 52-58.
22. Kero, B., Russell, L., Tsur, S., Shen, W.M., 1995. An overview of data mining technologies. The KDD Workshop in the 4th International Conference on Deductive and Object-Oriented Databases, Singapore.
23. Kodratoff Y. Introduction to Machine Learning (1988), Pitman Publishing, London
24. Mitkas, P.A., Symeonidis, A.L., Kechagias, D., Athanasiadis, I., 2002. An agent framework for dynamic agent retraining: agent academy. Workshop on Challenges and Achievements in e-business and e-work, Prague, pp. 757-764.
25. Mitkas, P.A., Kehagias, D., Symeonidis, A.L., Athanasiadis, I.N., 2003. A framework for constructing multi-agent applications and training intelligent agents. In: Proceedings of the Fourth International workshop on Agent-Oriented Software Engineering (AOSE-2003), held at Autonomous Agents & Multi-Agent Systems (AAMAS 2003), Melbourne, Australia, Springer, Berlin, pp. 1-16.
26. Mohammadiam M. Intelligent Agents for Data Mining and Information Retrieval (2004), Idea Group Inc.
27. Piatetsky-Shapiro G., and Frawley W.J. Knowledge Discovery in Databases (1992), MIT Press, Cambridge, MA
28. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann, San Mateo
29. Shapiro J.F. Bottom-up vs. top-down approaches to supply chain modelling. In: Tayur S., Ganeshan R., and Magazine M. (Eds). Quantitative Models for Supply Chain Management (1999), Kluwer, Massachusetts
30. Stolfo, S.J., Prodromidis, A.L., Tselepis, S., Lee, W., Fan, D.W., Chan, P.K., 1997. Jam: Java agents for meta-learning over distributed databases. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, AAAI Press, Newport Beach, CA, pp. 74-81.
31. Symeonidis A.L., and Mitkas P.A. Agent Intelligence through Data Mining (2005), Springer, USA. 0-387-24352-6
32. Symeonidis A.L., Kehagias D., and Mitkas P.A. Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques. Journal of Expert Systems with Applications 25 4 (2003) 589-602
33. Talavera, L., Cortes, U., 1997. Inductive hypothesis validation and bias selection in unsupervised learning. In: Proceedings of the Fourth European Symposium on the Validation and Verification of Knowledge Based Systems (EUROVAV-97), Leuven, Belgium, pp. 169-179.
34. Witten H.I., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (1999), Morgan Kaufmann, New Zealand
35. Wooldridge M. Intelligent agents. In: Weiss G. (Ed). Multiagent Systems (1999), MIT Press, Cambridge, MA
36. Zhang Z., Zhang C., and Zhang S. An agent-based hybrid framework for database mining. Journal of Applied Artificial Intelligence 17 (2003) 383-398