Putting the crowd to work in a knowledge-based factory
Putting the crowd to work in a knowledge-based factory
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DOI：
10.1016/j.aei.2010.05.011
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
J. Corney;C. Torres-Sánchez;A. Jagadeesan;Xiu-Tian Yan;W. Regli;H. Medellín
中科院分区：
文献类型：
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作者：
J. Corney;C. Torres-Sánchez;A. Jagadeesan;Xiu-Tian Yan;W. Regli;H. Medellín
尽管研究人员已经开发了许多推理和知识表示的计算方法，但它们的实现始终仅限于为其创建定制知识库或算法的特定应用（例如装配规划、故障诊断或生产调度）。然而，“云计算”使得机器智能使用的物理位置和内部流程变得无关紧要。换句话说，互联网鼓励将功能流程视为“黑匣子”，用户只需关心提出正确的问题并解释答案即可。提出问题的系统不需要知道答案是如何生成的，只需要知道它们在适当的时间范围内可用即可。本文提出，众包可以提供在线“黑匣子”推理能力，在灵活性和范围方面远远超过当前人工智能技术（即遗传算法、神经网络、基于案例的推理）的能力。本文描述了如何在三种不同的推理场景中部署众包来执行涉及大量隐性（例如非形式化）知识的工业任务。第一项研究报告了应用众包来识别 3D CAD 模型的规范视图。定性结果表明，匿名、互联网、劳动力对 3D 几何有很好的理解。建立了这种基本能力后，第二个实验评估了人群判断 3D 组件相似性的能力。结果与已发布的基准的比较显示出高度的一致性。最后，在 2D 嵌套任务中对互联网劳动者的表现进行了量化，发现他们的表现优于报道的计算算法。在所有这些情况下，结果都会在几个小时内返回，本文得出的结论是，众包有可能广泛应用于 CAD/CAM 中的几何问题解决。
Although researchers have developed numerous computational approaches to reasoning and knowledge representation, their implementations are always limited to specific applications (e.g. assembly planning, fault diagnosis or production scheduling) for which bespoke knowledge bases or algorithms have been created. However, “cloud computing” has made irrelevant both the physical location and internal processes used by machine intelligence. In other words, the Internet encourages functional processes to be treated as ‘black boxes’ with which users need only be concerned with posing the right question and interpreting the response. The system asking the questions does not need to know how answers are generated, only that they are available in an appropriate time frame. This paper proposes that Crowdsourcing could provide on-line, ‘black-box’, reasoning capabilities that could far exceed the capabilities of current AI technologies (i.e. genetic algorithms, neural-nets, case-based reasoning) in terms of flexibility and scope. This paper describes how Crowdsourcing has been deployed in three different reasoning scenarios to carry out industrial tasks that involve significant amounts of tacit (e.g. unformalised) knowledge. The first study reports the application of Crowdsourcing to identify canonical view of 3D CAD models. The qualitative results suggest that the anonymous, Internet, workforce have a good comprehension of 3D geometry. Having established this basic competence the second experiment assesses the Crowd’s ability to judge the similarity of 3D components. Comparison of the results with published benchmarks shows a high degree of correspondence. Lastly the performance of the Internet labourers is quantified in a 2D nesting task, where their performance is found to be superior to reported computational algorithms. In all these cases results were returned within a couple of hours and the paper concludes that there is potential for broad application of Crowdsourcing to geometric problem solving in CAD/CAM.