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Hybrid human-computing distributed sense-making: Extending the SOA paradigm for dynamic adjudication and optimization of human and computer roles.

机译：混合人工计算的分布式感知：扩展SOA范式，用于动态裁定和优化人机角色。

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摘要

In many evolving systems, inputs can be derived from both human observations and physical sensors. Additionally, many computation and analysis tasks can be performed by either human beings or artificial intelligence (AI) applications. For example, weather prediction, emergency event response, assistive technology for various human sensory and cognitive impairments, individual and community medical systems, energy efficient buildings/processes, and a host of other complex management and sense-making applications have the potential to be implemented as hybrid human/computing systems in which: (1) observational data can be provided by either physical sensors or humans acting as observers (or a combination of such input), and (2) sense-making can be performed by either automated inference algorithms (computer automated reasoning/pattern recognition) or by human cognition (or both). This category of hybrid system (referred to as "hard and soft information fusion") has wide-ranging promise for analysis of both physical data and abstract concepts. However, there are many challenges related to the effective storage, representation, and transmission of the vastly heterogeneous data necessary for scalable, loosely-coupled service-based communication between physical sensors, human observers, AI-based machine cognition tools, and human analysts. Additionally, there is currently a lack of techniques for adjudicating which tasks should be assigned to humans and which should be assigned to machine/ computer systems.;This research explores the current state of the art in distributed hard and soft information fusion and seeks to address the above-mentioned gaps and challenges through a novel integration of paradigms and techniques such as service oriented architecture (SOA), multi-agent software systems (MAS), complex event processing (CEP), sonification (auditory display), message oriented middleware (MOM), and community standard data representation. Additionally, it provides a prototype system implementation and a simulation experiment to evaluate the efficacy of the proposed techniques.
机译：在许多不断发展的系统中，输入可以从人类观察和物理传感器中获得。另外，许多计算和分析任务都可以由人类或人工智能（AI）应用程序执行。例如，天气预报，紧急事件响应，各种人类感官和认知障碍的辅助技术，个人和社区医疗系统，节能建筑/过程以及许多其他复杂的管理和感官应用程序都有可能实现作为混合的人机/计算机系统，其中：（1）观察数据可以由物理传感器或充当观察者的人（或此类输入的组合）提供，并且（2）感官可以由自动推理算法执行（计算机自动推理/模式识别）或人类认知（或两者）。这类混合系统（称为“硬信息和软信息融合”）具有广泛的前景，可用于分析物理数据和抽象概念。但是，在物理传感器，人类观察者，基于AI的机器认知工具和人类分析人员之间进行可伸缩，松耦合的基于服务的通信所需的巨大异构数据的有效存储，表示和传输方面，存在许多挑战。此外，目前缺乏判断哪些任务应该分配给人类，哪些任务应该分配给机器/计算机系统的技术;该研究探索了分布式硬和软信息融合的最新技术，并试图解决通过将范式和技术进行新颖的集成来克服上述差距和挑战，例如面向服务的体系结构（SOA），多代理软件系统（MAS），复杂事件处理（CEP），超音波（听觉显示），面向消息的中间件（ MOM）和社区标准数据表示形式。此外，它提供了原型系统实现和仿真实验，以评估所提出技术的有效性。

著录项

  • 作者

    Rimland, Jeffrey C.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Information Science.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 183 p.
  • 总页数 183
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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