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

Author

Ichimura, T. ; Tanabe, Kazuki

Author_Institution

Prefectural Univ. of Hiroshima, Hiroshima, Japan

fYear

2013

fDate

13-13 July 2013

Firstpage

21

Lastpage

26

Abstract

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.

Keywords

Petri nets; cognition; fuzzy set theory; pattern classification; psychology; EGC; FV missing value; case frame representation classification; dialog; effective learning method; emotion eliciting condition theory; emotion generating calculation method; favorite value estimation; fuzzy Petri net; personal taste information; speaker taste information learning method; Cognition; Databases; Equations; Learning systems; Mathematical model; Production; Psychology; Emotion Generating Calculations; Favorite Value; Fuzzy Petri Net; Learning personal taste information; Mental State Transition Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence & Applications (IWCIA), 2013 IEEE Sixth International Workshop on

Conference_Location

Hiroshima

ISSN

1883-3977

Print_ISBN

978-1-4673-5725-8

Type

conf

DOI

10.1109/IWCIA.2013.6624777

Filename

6624777