Title：Grey Theory in Subjective Uncertainty
Speecher：Arjab Singh Khuman

Date/Time:：3.18 15:00-16:00
Location:：Jiangjun Rd. Campus  D2210

Abstract：

        The notion of grey theory to some is foreign and rather alien, however, it is aspects of grey theory that have allowed for my research to link together the subjective nature of perception based uncertainty, to that of objective based inference. The prowess and functionality of grey theory has many advantages, and lends itself quite well to be hybridised with other existing concepts. My research is predominately associated to the quantification of perception based uncertainty, which itself is inherently riddled with uncertainty. The problem is maintaining the information and sentiment captured from a snapshot; if the information is subjective, it needs to be observed from an objective means to provide for a detailed inference which can be repeated and contrasted. The use of R-fuzzy sets in capturing the uncertainty is paramount to my research, for no information is lost and all the nuances that are observed are preserved in the membership sets. The problem is now linking it to the likes of grey incidence analysis, for it is this component of grey theory that provides a metric based on inference between 2 comparable sequences. It is only with the use of my significance measure that we can bridge together R-fuzzy’s uncertainty handling to that of grey’s incidence analysis capabilities, in providing a similarity index, from which greater detail can be garnered and ergo, a more detailed response given. The significance measure from my research is paramount and acts as the keystone in my uncertainty framework - The R-fuzzy grey analysis framework, without it, the benefits of grey theory cannot be utilised. As it will be seen from my presentation, the framework itself has the applicability to be deployed in a multitude of different environments and domains.
Brief introduction of the speecher ：
        Arjab Singh Khuman, or Archie Singh, as he is also known as, completed his BSc in Computer Science in 2009, his MSc in Intelligent Systems in 2011, and his PhD in Computer Science 2017. All which were undertaken at De Montfort University, where he is currently employed within Institute of Artificial Intelligence as an academic and researcher. His research interests are mainly concerned with uncertainty quantification and paradigms with hybridised concepts, with a particular interest being placed on human-based subjective perception.