Agents trained by learning techniques provide a powerful approximation of active solutions for naive approaches. In this study using B - Trees implying reinforced learning the data search for information retrieval is moderated to achieve accuracy with minimum search time. The impact of variables and tactics applied in training are determined using reinforcement learning. Agents based on these techniques perform satisfactory baseline and act as finite agents based on the predetermined model against competitors from the course. © 2013 SPIE.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@1ef2ef16 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@5f2e9d39 Through org.apache.xalan.xsltc.dom.DOMAdapter@6d543a00; Conference Code:97683
Sa Bhuvaneswari and Vignashwaran, Rb, “B - Tree search reinforcment learning for model based intelligent agent”, in Proceedings of SPIE - The International Society for Optical Engineering, Singapore, 2013, vol. 8768.