计算机与数字工程2018，Vol.46Issue(5)：851-856,889,7.DOI:10.3969/j.issn.1672-9722.2018.05.001
强规划的最小期望权值求解算法
Algorithm of Strong Programming Minimal Expectation Weight
摘要
Abstract
With the rapid development of artificial intelligence,the uncertain programming problem in intelligent planning has gradually become a hot topic.In uncertain systems,due to the influence of external interference factors,the state transition and the arrival of the results are uncertain,and performing actions need cost a certain price for uncertainty transfer system.To solve this problem,the weight is assigned to the action in uncertain system and probability is used to represent the uncertainty of state transi-tion.A method of solving strong planning with minimal expectation weight is designed based on the proposed concept of expectation weight for strong planning.First,the algorithm adds the target state set into the search state set and uses reverse search to find the strong planning solution corresponding to minimal expected weight;in the search process,the state corresponding to minimum ex-pectation weights should be added to the searched state,and then this paper updates the state set which is not searched.The above steps are repeated until the searched set unchanged.关键词
人工智能/不确定规划/强规划解/概率分布/期望权值/反向搜索Key words
artificial intelligence/uncertain programming/strong planning solution/probability distribution/expected weight/reverse search分类
信息技术与安全科学引用本文复制引用
袁润,文中华,戴良伟,陈秋茹..强规划的最小期望权值求解算法[J].计算机与数字工程,2018,46(5):851-856,889,7.基金项目
国家自然科学基金项目(编号:61272295,61105039,61202398) （编号:61272295,61105039,61202398）
湘潭大学智能计算与信息处理教育部重点实验室、湖南省重点学科建设项目(编号:0812)资助. （编号:0812）
