【摘 要】
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With the complexity of the composition process and the rapid growth of candidate services,real-izing optimal or near-optimal service composition is an urgent pr
【机 构】
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School of Geography and Information Engineering,China University of Geosciences,Wuhan 430074,P.R.Chi
论文部分内容阅读
With the complexity of the composition process and the rapid growth of candidate services,real-izing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To ad-dress these challenges,the geographic information service composition(GISC)problem as a se-quential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS)and a temporal-difference(TD)learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capabili-ty at runtime,based on the environment and the status of component services.The simulation exper-iment is performed to demonstrate the effectiveness and efficiency through learning quality and per-formance.
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