Empirical Methods for Modelling Persuadees in Dialogical Argumentation

被引:9
作者
Hunter, Anthony [1 ]
Polberg, Sylwia [1 ]
机构
[1] UCL, Dept Comp Sci, London, England
来源
2017 IEEE 29TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2017) | 2017年
基金
英国工程与自然科学研究理事会;
关键词
ABSTRACT ARGUMENTATION; PROBABILISTIC ARGUMENTATION; PERSUASION; INTERFACES; SEMANTICS; DIALOGUES; BEHAVIOR; SYSTEMS;
D O I
10.1109/ICTAI.2017.00066
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
For a participant to play persuasive arguments in a dialogue, s/he may create a model of the other participants. This may include an estimation of what arguments the other participants find believable, convincing, or appealing. The participant can then choose to put forward those arguments that have high scores in the desired criteria. In this paper, we consider how we can crowd-source opinions on the believability, convincingness, and appeal of arguments, and how we can use this information to predict opinions for specific participants on the believability, convincingness, and appeal of specific arguments. We evaluate our approach by crowd-sourcing opinions from 50 participants about 30 arguments. We also discuss how this form of user modelling can be used in a decision-theoretic approach to choosing moves in dialogical argumentation.
引用
收藏
页码:382 / 389
页数:8
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