Queries that are not indicative of real information needs are a major problem for information retrieval systems. In this work we study how individual learning helps adaptive agents, when searching for information in a distributed environment, to modify incomplete queries in order to improve their retrieving performance. Two learning procedures, occurring in two different levels, will be proposed and their effect will be studied in several situations. Preliminary results show that changes induced by learning in the query vector of adaptive agents, provide an important advantage and enable them to make correct decisions about how to deal with this problem.
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Year 2013 : 1 citations
BL Iantovics, CB Zamfirescu. ERMS: AN EVOLUTIONARY REORGANIZING MULTIAGENT SYSTEM. International Journal of Innovative Computing, Information and Control, 2013.
Year 2009 : 1 citations
P. Cristea (2009). Application of Neural Networks in Image Processing and Visualization. In Geo-Spatial Visual Analytics: Geographic Information Processing and Visual Analytics for Environmental Security, R. Amicis et. at. (Eds.), pp. 59-71, Springer.
Year 2008 : 1 citations
PD Cristea. Use of intelligent evolutionary agents in the analysis of genomic signals. Proceedings of the 10th WSEAS International Conference, 2008.
Year 2005 : 1 citations
Kushchu, I. (2005). Web-Based Evolutionary and Adptive Information Retrieval, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 2, pp. 117-125
Year 2003 : 1 citations
D. Mirikitani, I. Kushchu (2003). E. Coli Search: Self Replicating Agents for Web Based Information Retrieval. In Proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL-2003), pp. 622-629, LNCS 2690, Springer.
Year 2000 : 1 citations
Cristea, P. Arsene, A. e Nitulescu, B. (2000). Evolutionary Intelligent Agents. In Proceedings of the Conference on Evolutionary Computation, Special Session on Evolutionary Intelligent Agents (CEC-2000), pp. 1320-1328, San Diego, EUA
