Title

Shafer Theory Combination Rules in a Framework for Identity

Student Classification

Junior

Faculty Mentor

Dr. Albert Esterline

Department

Computer Science

Document Type

Poster

Publication Date

Spring 2019

Abstract

Our research is focused on taking relevant information and using it to help form an identity hypothesis that could have been involved in a particular event. Information is gathered from different types of situations, constraint, utterance, and resource situation. Situation theory is involved when mentioning these types of situations. The Semantic Web and its standards, also used in our research, is used to maintain the structure of information from this theory. After encoding the given information, we will be able to apply Dempster Shafer Theory. Dempster Shafer theory is to help our identity hypotheses. Mass values are given to each person who could potentially be involved in the event. The mass range is from 0.0 to 1.0. The mass represents the amount of evidence the person is in. In our framework, we are interested in incorporating more rules to obtain accuracy. Because of flexibility, the programming language Python is used.

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