基于模糊Petri网的情感生成计算中喜爱值的估计

2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2013-07-13 DOI:10.1109/IWCIA.2013.6624777

T. Ichimura, Kousuke Tanabe

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引用次数: 0

摘要

基于情绪诱发条件理论的情绪生成计算方法可以判断一个事件是否引起愉悦感，并量化该事件下的愉悦程度。Case Frame表示形式的事件分为12种类型的计算。然而，作为个人品味信息的喜好值(Favorite Value, FV)是EGC的弱点。为了改善这一问题，本文尝试建立一种从对话中学习说话人口味信息的学习方法。特别是，该学习方法采用模糊Petri网对具有未知FV的单词寻找合适的FV。本文讨论了当FV存在缺失值时，改进EGC弱点的有效学习方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An estimation of favorite value in emotion generating calculation by Fuzzy Petri Net

Emotion Generating Calculations (EGC) method based on the Emotion Eliciting Condition Theory can decide whether an event arouses pleasure or not and quantify the degree under the event. An event in the form of Case Frame representation is classified into 12 types of calculations. However, the weak point in EGC is Favorite Value (FV) as the personal taste information. In order to improve the problem, this paper challenges to establish a learning method to learn speaker's taste information from dialog. Especially, the learning method employs Fuzzy Petri Net to find an appropriate FV to a word which has the unknown FV. This paper discusses the effective learning method to improve a weak point of EGC when a missing value of FV exists.

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2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)

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