Efficient Learning Algorithms for Problems with Acyclic State Spaces and their Application to Reverse Logistics

该补助金为开发适合非循环状态空间决策问题的权宜、定制的机器学习算法提供资金，并将其应用于最优拆卸规划（ODP）的应用领域。所提出的研究的理论框架是基于以下事实：（i）所考虑的状态空间的非循环性质使得能够明确表征学习过程的动态特性，以及（ii）来自被称为排名选择的统计推断领域的一些结果。此外，嵌入这些统计结果在拟议的方法框架引起了一些马尔可夫决策过程（MDP）类型的问题，目前尚未探索，他们拥有一些非常有趣的特殊结构和显着的实际意义。拟议的研究的最后阶段将寻求将导出的结果应用到ODP问题，并将其集成到目前正在开发的计算测试平台中。预期的理论发展和上述计算测试平台都将通过专门的网站向更广泛的社区提供。如果成功的话，这项研究的结果将从理论和实践的角度扩展机器学习领域目前已有的成果。在理论方面，预期的结果将促进利用这些问题的许多子类的特殊结构的可能性，以提高分析的易处理性，效率和严谨的开发解决方案。在实践方面，预期的结果将导致权宜定制的学习算法，可证明建立的属性为整个类的决策问题与非循环状态空间，一般来说，ODP问题，特别是。最后，在马尔可夫决策过程领域的额外贡献，通过制定和研究的特殊结构的MDP在前面的段落中提到的;除了他们的外观在机器学习的上下文中的这项工作，这些MDP的出现也在许多其他的应用环境中，涉及随机结果的顺序实验的最佳设计。

This grant provides funding for the development of expedient, customized machine learning algorithms that are appropriate for decision problems with acyclic state spaces, and their implementation to the application area of optimal disassembly planning (ODP). The theoretical framework for the proposed research is based on (i) the fact that the acyclic nature of the considered state spaces enables the explicit characterization of the dynamics of the learning process and (ii) some results coming from the area of statistical inference known as ranking & selection". Furthermore, the embedding of these statistical results in the proposed methodological framework gives rise to some Markov Decision Process (MDP)-type of problems, that are currently unexplored and they possess some very interesting special structure and significant practical relevance. The last stage of the proposed research will seek to apply the derived results to the ODP problem, and integrate them in a computational testbed for this problem, that is currently under development. Both, the expected theoretical developments and the aforementioned computational testbed will be accessible to the broader community through a dedicated Website. If successful, the results of this research will extend the currently existing results in the area of machine learning, from both, a theoretical and a practical standpoint. On the theoretical side, the expected results will promote the possibility of exploiting the special structure underlying many sub-classes of these problems, in order to enhance the analytical tractability, efficiency and rigor of the developed solutions. On the practical side, the expected results will lead to expedient customized learning algorithms with provably established properties for the entire class of decision problems with acyclic state spaces, in general, and the ODP problem, in particular. Finally, additional contributions are expected in the area of Markov Decision Processes, through the formulation and study of the specially structured MDP's mentioned in the previous paragraph; beyond their appearance in the machine learning context of this work, these MDP's arise also in many other application contexts that involve the optimal design of sequential experiments with random outcomes.