Algorithm of Strong Programming Minimal Expectation Weight
YUAN Run
WEN Zhonghua
DAI Liangwei
CHEN Qiuru
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.
Keywords：artificial intelligenceuncertain programmingstrong planning solutionprobability distributionexpected weightreverse search
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 851-856,889 )