Applying Reinforcement Learning for Resolving Ambiguity in Service Composition

Alexander Jungmann, F. Mohr, B. Kleinjohann
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引用次数: 3

Abstract

Automatically composing service-based software solutions is still a challenging task. Functional as well as non-functional properties have to be considered in order to satisfy individual user requests. Regarding non-functional properties, the composition process can be modeled as optimization problem and solved accordingly. Functional properties, in turn, can be described by means of a formal specification language. State-space based planning approaches can then be applied to solve the underlying composition problem. However, depending on the expressiveness of the applied formalism and the completeness of the functional descriptions, formally equivalent services may still differ with respect to their implemented functionality. As a consequence, the most appropriate solution for a desired functionality can hardly be determined without considering additional information. In this paper, we demonstrate how to overcome this lack of information by means of Reinforcement Learning. In order to resolve ambiguity, we expand state-space based service composition by a recommendation mechanism that supports decision-making beyond formal specifications. The recommendation mechanism adjusts its recommendation strategy based on feedback from previous composition runs. Image processing serves as case study. Experimental results show the benefit of our proposed solution.
应用强化学习解决服务组合中的歧义
自动组合基于服务的软件解决方案仍然是一项具有挑战性的任务。为了满足单个用户的请求,必须考虑功能性和非功能性属性。对于非功能属性,组合过程可以建模为优化问题并进行求解。反过来,功能属性可以通过形式规范语言来描述。然后可以应用基于状态空间的规划方法来解决潜在的组合问题。然而,根据应用的形式主义的表达性和功能描述的完整性,形式上等价的服务在其实现的功能方面仍然可能有所不同。因此,如果不考虑其他信息,就很难确定所需功能的最合适的解决方案。在本文中,我们演示了如何通过强化学习来克服这种信息缺乏。为了解决歧义,我们通过推荐机制扩展了基于状态空间的服务组合,该机制支持超出正式规范的决策。推荐机制根据以前的组合运行反馈调整其推荐策略。图像处理作为案例研究。实验结果表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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