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An attractive feature of the Connection Manager Intelligence Agent is its use of network traffic and multi-attribute behavior to locate the best network devices. This study has integrated this agent with a user interface; a network connection handoff; wired and wireless network device drivers; network management applications of the (plug-in) play interface; module-to-module communication authentication; and a DBus for added versatility. To reduce the time that developers of embedded systems spend on the software engineering of this module and to achieve rapid operational efficiency, an Open Source platform, such as MeeGo or Android, must be used. This study has implemented an interactive interface through the function (based on Fuzzy-AHP) of acquisition user behavior and machine designers, boosting iterations for User-Case. The algorithm maintains a set of weights as a distribution class table of cases, as in the parameter learning by user-case; it is quite possible that the expectation–maximization of maximum probability model can be classified by user behavior. In this study, user interaction showed that the agent satisfactorily matched user intent.
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Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669. CrossRef
Broder, A., & Schiffer, S. (2003). Take the best versus simultaneous feature matching: Probabilistic inferences from memory and effects of representation format. Journal of Experimental Psychology-General, 132, 277–293. CrossRef
Yalabik, I., & Fatos, T. Y.-V. (2007). A pattern classification approach for boosting with genetic algorithms. In IEEE conference ICIS, 22nd international symposium (pp. 1–6).
Krovi, R., Graesser, A. C., & Pracht, W. E. (1999). Agent behaviors in virtual negotiation enviroments. IEEE Transactions on Systems, Man, and Cybernetics, 29, 15–25. CrossRef
Lau, R. Y. K. (2007). Fuzzy domain ontology discovery for business knowledge management. IEEE Intelligent Informatics Bulletin, IEEE Computer Society, 8(1), 29–41.
Saaty, T. L., Rogers, P. C., & Pell, R. (1980). Portfolio selection through hierarchies. Journal of Portfolio Management, 16–21; Spring.
Sgora, A., Gizelis, C. A., & Vergados, D. D. (2011). Network selection in a WiMAX-WiFi environment. Elsevier B.V. Journal of Pervasive and Mobile Computing, 7(5), 584–594.
Russell, S. J., & Norvig, P. (2006). Artificial intelligence: A modern approach (3rd ed.). Upper Saddle River: Pearson Education.
Zeng, L. (2009). A model for fuzzy multiple attribute group decision making and fuzzy simulation algorithm. In AICI conference on 2009, IEEE international conference of intelligence and computational intelligence (vol. 04).
Zhang, R., Huang, L., & Xiao, M. (2010). Security evaluation for wireless network based on Fuzzy-AHP with variable weight. In IEEE 2th NSWCTC international conference networks security wireless communications and trusted computing (vol. 02, pp. 24–25), April, 2010.
Wang, H., & Song, R. (2013). Distributed Q-learning for interference mitigation in self-organised femtocell networks: Synchronous or asynchronous? Wireless Personal Communications, 71(4), 2491–2506. CrossRef
Karaca, O., Sokullu, R., Prasad, N. R., & Prasad, R. (2012). Application oriented multi criteria optimization in WSNs using on AHP. Wireless Personal Communications, 65(3), 689–712. CrossRef
Gao, T., Jin, R. C., Song, J. Y., Tai Bing, X., & Wang, L. D. (2012). Energy-efficient cluster head selection scheme based on multiple criteria decision making for wireless sensor networks. Wireless Personal Communications, 63(4), 871–894. CrossRef
Charilas, D. E., Panagopoulos, A. D., & Markaki, O. I. (2014). A unified network selection framework using principal component analysis and multi attribute decision making. Wireless Personal Communications, 74(1), 147–165. CrossRef
Chen, S. C., Yang, C. C., Lin, W. T., Yeh, T. M., & Lin, Y. S. (2007). Construction of key model for knowledge management system using AHP-QFD for semiconductor industry in Taiwan. Journal of Manufacturing Technology Management, 18, 576–598. CrossRef
Lee, J., & Xue, N. L. (1999). Analyzing user requirements by use cases: A goal- driven approach. IEEE Software, 16, 92–101. CrossRef
Leon Lee, C.-H., & Liu, A. (2009). A case-based service request interpretation approach for digital homes. In IEEE SMC conference (pp. 789–794).
Kahraman, C., Cebeci, U., & Ulukan, Z. (2003). Multi-criteria supplier selection using fuzzy AHP. Journal of Enterprise Information Management, Logistic information Management, 16(6), 382–394. CrossRef
Triantaphyllou, E., & Baig, K. (2005). The impact of aggregating benefit and cost criteria in four MCDA methods. IEEE Transactions on Engineering Management, 52(2), 213–226. CrossRef
Ayağ, Z., & Özdemir, R. G. (2010). An intelligent approach to machine tool selection through fuzzy analytic network process. Journal of Intelligent Manufacturing, Business and Economics, 22(2), 163–177.
Zaim, S., Sevkli, M., & Tarim, M. (2003). Fuzzy analytic hierarchy based approach for supplier selection. Journal of Euromarketing, 12(3 & 4), 147–176. CrossRef
Dağdeviren, M., & Eraslan, E. (2008). Priority determination in strategic energy policies in Turkey using analytic network process with group decision making. International Journal of Energy Research, 32(11), 1047–1057. CrossRef
Buckley, J. J. (1985). Fuzzy hierarchical analysis. In Conference on Rec. 1985 IEEE international conference on fuzzy sets and systems (vol. 17, pp. 233–247).
Teng, J. Y., & Tzeng, G. H. (1993). Transportation investment project selection with fuzzy multi-objective. Transporttation Planning and Technology, 17, 91–112. CrossRef
Bishop, C. M. (2006). Pattern recognition and machine learning (p. 14, pp. 38–43). Secaucus, NJ: Springer.
Katayama, K., & Narihisa, H. (March 2005). Reinforcement learning agent with primary knowledge designed by analytic hierarchy process. In Proceedings of the SAC’05 ACM symposium on applied computing, New York.
Dikaiakos, M., Stassopoulou, A., & Papageorgiou, L. (2003). Characterizing crawler behavior from Web server access logs. In E-Commerce and Web technologies, in proceedings of the 4th Lecture Notes in computer science series, international conference on electronic commerce and Web technologies (vol. 2738, pp. 369–378). Berlin: Springer.
Freund, Y. (September 1995). Boosting a weak learning algorithm by majority. In inform and computation conference (vol. 121, no. 2) (September 1995), pp. 256–285; an extended abstract appeared in proceeding of the 3th annual computational learning theory, 1990.
Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55, 119–139.
Ferber, J. (1999). Multi-agent systems: An introduction to distributed artificial intelligence. Harlow: Addison Wesley Longman.
Magnenat, S., & Mondada, F. (2009). ASEBA meets D-Bus: From the depths of a low-level event-based architecture. In IEEE TC-Soft workshop, event-based systems in robotics (EBS-RO).
Burton, R. (July 27 2004). Connect desktop apps using d-bus. http://www.ibm.com/developerworks/linux/library/l-dbus.html.
- Classifier Learning and Decision Making for a Connection Manager on a Heterogeneous Network
- Springer US
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