Empirical study of an intelligent argumentation system in MCDM

Liu, Xiaoqing; Wanchoo, Rubal; Arvapally, Ravi Santosh

doi:10.1109/cts.2011.5928674

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…This exchange of information in groups leads to collective decision-making. Please see our article (Liu, Wanchoo, and Arvapally 2011) to know more about the opinions and goals of stakeholders in an argumentation process.…”

Section: Elements Of An Argumentation Treementioning

confidence: 99%

Polarisation assessment in an intelligent argumentation system using fuzzy clustering algorithm for collaborative decision support

Arvapally¹,

Liu²

2013

Argument & Computation

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We developed an on-line intelligent argumentation system which facilitates stakeholders in exchanging dialogues. It provides decision support by capturing stakeholders' rationale through arguments. As part of the argumentation process, stakeholders tend to both polarise their opinions and form polarisation groups. The challenging issue of assessing argumentation polarisation had not been addressed in argumentation systems until recently. Arvapally, Liu, and Jiang [(2012), 'Identification of Faction Groups and Leaders in Web-Based Intelligent Argumentation System for Collaborative Decision Support', in Proceedings of International Conference on Collaborative Technologies and Systems] earlier developed a method to identify polarisation groups. These groups, however, tend to overlap to a certain degree; each stakeholder may be a member of multiple polarisation groups to varied degrees. Quantifying stakeholders' membership in multiple polarisation groups is an important issue in the argumentation for collaborative decision-making, which is not addressed earlier. We present a novel approach using fuzzy clustering algorithm to address this issue in this article. The method is evaluated using data sets produced from the discussions of 24 stakeholders. Experimental results indicate that our method is effective for both identifying polarisation groups and quantifying stakeholders' degree of membership in each polarisation group.

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Section: Elements Of An Argumentation Treementioning

confidence: 99%

Polarisation assessment in an intelligent argumentation system using fuzzy clustering algorithm for collaborative decision support

Arvapally¹,

Liu²

2013

Argument & Computation

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“…The data were generated through an argumentation process using the system developed in [19]. The experiment was conducted with a group of 24 stakeholders who were recruited from a software engineering class.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

“…Even though an IAS can improve effectiveness of an argumentation and facilitate the progress of collaboration in many aspects, empirical studies in [19] have shown that the effectiveness of an IAS is highly correlated to the number of participants (or stakeholders) and arguments. That is, when arguments keep accumulating in the system, the stakeholders need to spend more time and effort in reading and understanding previous arguments posted by the stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Argument placement recommendation and relevancy assessment in an intelligent argumentation system

Liu

Jiang

et al. 2013

2013 International Conference on Collaboration Technologies and Systems (CTS)

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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 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 in relevant argumentation thread. We also empirically evaluate the effectiveness of the proposed recommendation component, and the empirical results indicate it is effective according to a real dataset.

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“…An intelligent argumentation system enables a group of stakeholders to post problems under consideration and their solution alternatives (positions), and then to exchange arguments surrounding those alternatives to reach a consensus. During the dialog process, as stakeholders exchange arguments, some change their opinions, some gain confidence in their opinions, and some lose confidence in their opinions 1 . Each stakeholder's opinion within an argumentation process must be considered for collaborative decision support.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous papers on online intelligent argumentation, we investigated a number of research issues, such as polarized argumentation groups, fuzzy logic‐based argumentation reduction, argumentation for multicriteria decision support, and priority assessment 1,8–10 . However, we had not previously investigated outlier opinion detection and published any research results in the above papers, and to our knowledge, no other researchers have investigated outlier detection in online intelligent argumentation.…”

Section: Introductionmentioning

confidence: 99%

Identifying outlier opinions in an online intelligent argumentation system

Arvapally

Liu

Nah

et al. 2017

Concurrency and Computation

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Summary Online argumentation systems enable stakeholders to post their problems under consideration and solution alternatives and to exchange arguments over the alternatives posted in an argumentation tree. In an argumentation process, stakeholders have their own opinions, which very often contrast and conflict with opinions of others. Some of these opinions may be outliers with respect to the mean group opinion. This paper presents a method for identifying stakeholders with outlier opinions in an argumentation process. It detects outlier opinions on the basis of individual stakeholder's opinions, as well as collective opinions on them from other stakeholders. Decision makers and other participants in an argumentation process therefore have an opportunity to explore the outlier opinions within their groups from both individual and group perspectives. In a large argumentation tree, it is often difficult to identify stakeholders with outlier opinions manually. The system presented in this paper identifies them automatically. Experiments are presented to evaluate the proposed method. Their results show that the method detects outlier opinions in an online argumentation process effectively.

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Empirical study of an intelligent argumentation system in MCDM

Cited by 5 publications

References 8 publications

Polarisation assessment in an intelligent argumentation system using fuzzy clustering algorithm for collaborative decision support

Polarisation assessment in an intelligent argumentation system using fuzzy clustering algorithm for collaborative decision support

Argument placement recommendation and relevancy assessment in an intelligent argumentation system

Identifying outlier opinions in an online intelligent argumentation system

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