An Estimation of Favorite Value in Emotion Generating Calculation by Fuzzy Petri Net
This work addresses a specific bottleneck in emotion modeling for AI systems, but it is incremental as it builds on existing Emotion Eliciting Condition Theory.
The paper tackled the problem of missing Favorite Values (FV) in Emotion Generating Calculations by proposing a learning method using Fuzzy Petri Net to estimate FV from dialog data, resulting in improved accuracy for emotion quantification.
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.