Corney, Jonathan and Torres-Sanchez, Carmen and Jagadeesan, Ananda Prasanna and Yan, Xiu and Regli, W.C. and Medellin, H. (2010) Putting the crowd to work in a knowledge-based factory. Advanced Engineering Informatics, 24 (3). pp. 243-250. ISSN 1474-0346Full text not available in this repository. Request a copy from the Strathclyde author
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.
|Keywords:||crowdsourcing, 2D nesting, shape similarity, canonical views, geometric reasoning, machine learning, Engineering design, Artificial Intelligence, Information Systems|
|Subjects:||Technology > Engineering (General). Civil engineering (General) > Engineering design|
|Department:||Faculty of Engineering > Design, Manufacture and Engineering Management|
|Depositing user:||Pure Administrator|
|Date Deposited:||06 May 2011 14:06|
|Last modified:||20 Apr 2017 13:14|