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Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition
ACM Transactions on Autonomous and Adaptive Systems ( IF 2.7 ) Pub Date : 2017-05-25 , DOI: 10.1145/3058592
Hongbign Wang 1 , Xin Chen 1 , Qin Wu 1 , Qi Yu 2 , Xingguo Hu 1 , Zibin Zheng 3 , Athman Bouguettaya 4
Affiliation  

Service-oriented architecture is a widely used software engineering paradigm to cope with complexity and dynamics in enterprise applications. Service composition, which provides a cost-effective way to implement software systems, has attracted significant attention from both industry and research communities. As online services may keep evolving over time and thus lead to a highly dynamic environment, service composition must be self-adaptive to tackle uninformed behavior during the evolution of services. In addition, service composition should also maintain high efficiency for large-scale services, which are common for enterprise applications. This article presents a new model for large-scale adaptive service composition based on multi-agent reinforcement learning. The model integrates reinforcement learning and game theory, where the former is to achieve adaptation in a highly dynamic environment and the latter is to enable agents to work for a common task (i.e., composition). In particular, we propose a multi-agent Q-learning algorithm for service composition, which is expected to achieve better performance when compared with the single-agent Q-learning method and multi-agent SARSA (State-Action-Reward-State-Action) method. Our experimental results demonstrate the effectiveness and efficiency of our approach.

中文翻译:

将强化学习与自适应服务组合的多智能体技术相结合

面向服务的体系结构是一种广泛使用的软件工程范例,用于应对企业应用程序中的复杂性和动态性。服务组合为实现软件系统提供了一种经济高效的方式,引起了工业界和研究界的极大关注。由于在线服务可能会随着时间的推移不断发展,从而导致高度动态的环境,因此服务组合必须具有自适应能力,以解决服务发展过程中的不知情行为。此外,服务组合还应该为大型服务保持高效,这在企业应用程序中很常见。本文提出了一种基于多智能体强化学习的大规模自适应服务组合新模型。该模型集成了强化学习和博弈论,前者是在高度动态的环境中实现适应,后者是使代理能够为共同任务(即组合)工作。特别是,我们提出了一种用于服务组合的多智能体 Q 学习算法,与单智能体 Q 学习方法和多智能体 SARSA(State-Action-Reward-State-Action)相比,有望取得更好的性能。 ) 方法。我们的实验结果证明了我们方法的有效性和效率。与单智能体 Q 学习方法和多智能体 SARSA(State-Action-Reward-State-Action)方法相比,有望取得更好的性能。我们的实验结果证明了我们方法的有效性和效率。与单智能体 Q 学习方法和多智能体 SARSA(State-Action-Reward-State-Action)方法相比,有望取得更好的性能。我们的实验结果证明了我们方法的有效性和效率。
更新日期:2017-05-25
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