教授 博士生导师
招生学科专业:
计算机科学与技术 -- 【招收博士、硕士研究生】 -- 计算机科学与技术学院
软件工程 -- 【招收硕士研究生】 -- 计算机科学与技术学院
电子信息 -- 【招收博士、硕士研究生】 -- 计算机科学与技术学院
性别:男
学历:清华大学
学位:工学博士学位
所在单位:计算机科学与技术学院/人工智能学院/软件学院
联系方式:13814535662
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所属单位:计算机科学与技术学院/人工智能学院/软件学院
发表刊物:Proc. - IEEE/ACIS Int. Conf. Softw. Eng. Res., Manag. Appl., SERA
摘要:Compositional stochastic model checking in the assume-guarantee style is a theoretically feasible way to alleviate the state explosion problem. The key for assume-guarantee reasoning is how to generate assumption. A main automated approach for assume-guarantee are based on learning assumptions. However, L∗-based learning assumptions for stochastic systems produces many intermediate results which need to be recorded. To overcome this, we propose a novel learning technique based on genetic algorithm for compositional stochastic model checking of Markov decision process, which is a randomized algorithm essentially. There are no intermediate results need to be recorded in the genetic algorithm-based learning algorithm, except the encoding of the problem domain and the training set. It can reduce the space complexity largely with respect to derivation algorithms. We implement a prototype tool for it and report encouraging results. © 2019 IEEE.
是否译文:否
发表时间:2019-05-01
合写作者:Ma, Yan,Liu, Yang
通讯作者:曹子宁