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dc.contributor.authorTian, Lezh_CN
dc.contributor.authorCao, Lang-Caizh_CN
dc.contributor.author曹浪财zh_CN
dc.date.accessioned2015-07-22T07:28:59Z
dc.date.available2015-07-22T07:28:59Z
dc.date.issued2014 Junezh_CN
dc.identifier.citationXi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2014,36(6):1201-1206zh_CN
dc.identifier.issn1001-506Xzh_CN
dc.identifier.other20142917947374zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/92983
dc.description.abstractThe discriminative model update (DMU) is a common algorithm for solving interactive dynamic influence diagrams (I-DIDs). The look-ahead method is used to give an improved discriminative model update algorithm which determines approximate behavior equivalence. Firstly, the models that are approximately behavior equivalent are clustered into a representative model set. Then the models within the representative model set are updated from top to bottom. In the updating process, only the models whose predictive behavior is different from others are updated.compared with the DMU algorithm, the proposed algorithm can quickly and effectively reduce the model's number, thus reducing the storage space and the running time of the computer, and improving the efficiency of the algorithm. The effectiveness of the proposed method is verified through experiments on the multi-agent tiger and multi-agent machine maintenance problems.zh_CN
dc.language.isoen_USzh_CN
dc.publisherChinese Institute of Electronicszh_CN
dc.source.urihttp://dx.doi.org/10.3969/j.issn.1001-506X.2014.06.29zh_CN
dc.subjectBioinformaticszh_CN
dc.titleImproved look-ahead based DMU algorithm for interactive dynamic influence diagramszh_CN
dc.typeArticlezh_CN


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