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首页> 外文会议>IFIP WG 12.5 International workshops on artificial intelligence applications and innovations;Mining Humanistic Data Workshop;Workshop on 5G-Putting Intelligence to the Network Edge >Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning
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Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning

机译：使用可传递性和追求目标的对象分区，优化自组织列表对列表

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The study of Self-organizing lists deals with the problem of lowering the average-case asymptotic cost of a list data structure receiving query accesses in Non-stationary Environments (NSEs) with the so-called "locality of reference" property. 'The de facto schemes for Adaptive lists in such Environments are the Move To Front (MTF) and Transposition (TR) rules. However, significant drawbacks exist in the asymptotic accuracy and speed of list re-organization for the MTF and TR rules. This paper improves on these schemes using the design of an Adaptive list data structure as a hierarchical data "sub "-structure. In this framework, we employ a hierarchical Singly-Linked-Lists on Singly-Linked-Lists (SLLs-on-SLLs) design, which divides the list data structure into an outer and inner list context. The inner-list context is itself a SLLs containing sub-elements of the list, while the outer-list context contains these sublist partitions as its primitive elements. The elements belonging to a particular sublist partition are determined using reinforcement learning schemes from the theory of Learning Automata. In this paper, we show that the Transitivity Pursuit-Enhanced Object Migration Automata (TPEOMA) can be used in conjunction with the hierarchical SLLs-on-SLLs as the dependence capturing mechanism to learn the probabilistic distribution of the elements in the Environment. The idea of Transitivity builds on the Pursuit concept that, injects a noise filter into the EOMA to filter divergent queries from the Environment, thereby increasing the likelihood of training the Automaton to approximate the "true" distribution of the Environment. By taking advantage of the Transitivity phenomenon based on the statistical distribution of the queried elements, we can infer "dependent" query pairs from non-accessed elements in the transitivity relation. The TPEOMA-enhanced hierarchical SLLs-on-SLLs schemes results in superior performances to the MTF and TR schemes as well as to the EOMA-enhanced hierarchical SLLs-on-SLLs schemes in NSEs. However, the results are observed to have superior performances to the PEOMA-enhanced hierarchical schemes in Environments with a Periodic non-stationary distribution but were inferior in Markovian Switching Environments.
机译：自组织列表的研究解决了降低列表数据结构在非平稳环境（NSE）中具有所谓的“参考位置”属性的平均情况下渐近成本的问题。在这样的环境中，自适应列表的实际方案是前移（MTF）和换位（TR）规则。但是，MTF和TR规则的渐近准确性和列表重新组织的速度存在重大缺陷。本文使用自适应列表数据结构作为分层数据“子”结构的设计对这些方案进行了改进。在此框架中，我们在单链接列表（SLLs-on-SLLs）设计中采用了分层的单链接列表（SLLs-on-SLLs）设计，该结构将列表数据结构分为外部和内部列表上下文。内部列表上下文本身就是包含列表子元素的SLL，而外部列表上下文包含这些子列表分区作为其原始元素。使用来自学习自动机理论的强化学习方案确定属于特定子列表分区的元素。在本文中，我们表明，传递性追踪增强对象迁移自动机（TPEOMA）可以与分层SLL-on-SLLs结合使用，作为依赖捕获机制，以了解环境中元素的概率分布。传递性的概念建立在追求概念的基础上，该概念将噪声过滤器注入EOMA中，以过滤来自环境的各种查询，从而增加了训练自动机以近似环境的“真实”分布的可能性。通过利用基于查询元素的统计分布的可及性现象，我们可以从与性关系中未访问的元素中推断“相关”查询对。 TPEOMA增强的SLL-on-SLLs分层体系比NTF的MTF和TR方案以及EOMA增强的SLL-on-SLLs分层体系具有更好的性能。但是，在具有周期性非平稳分布的环境中，观察到的结果比PEOMA增强的分层方案具有更好的性能，但在马尔可夫切换环境中则较差。

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