"Argumentation is a critical process for many social activities that need collaborative intelligence. Existing intelligent argumentation systems allow multiple stakeholders from distributed geographical locations to share their opinions and contribute to a decision making process. In the current system, a stakeholder needs to read all the existing arguments posted by other stakeholders before contributing his/her own ideas/arguments. However, when information accumulates and an argumentation network becomes considerably large, it will cost tremendous time and effort for the stakeholder to read and comprehend all existing arguments. In this paper, we propose methods to implement a argument placement recommendation component built into an intelligent argumentation system. The recommendation component can automatically assist a stakeholder to better understand the current state of an argumentation by summarizing and identifying a subset of existing arguments that are relevant to the stakeholder. Thus, our proposed work will allow a stakeholder to efficiently and effectively express his/her thoughts in an intelligent argumentation system. We also empirically evaluate the effectiveness of the proposed recommendation component, and the emperical results indicate that it is effective"--Abstract, page iii.
Liu, Xiaoqing Frank
M.S. in Computer Science
Missouri University of Science and Technology
vii, 27 pages
© 2013 Nian Liu, All rights reserved.
Thesis - Restricted Access
Library of Congress Subject Headings
Reasoning -- Computer simulation
Logic -- Computer simulation
Groupware (Computer software)
Print OCLC #
Electronic OCLC #
Link to Catalog RecordElectronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu:80/record=b10119041~S5
Liu, Nian, "Argumentation placement recommendation and relevancy assessment in an intelligent argumentation system" (2013). Masters Theses. 5993.