Modeling emotion and inference as a value calculation system

Miyata, Masahiro; Omori, Takashi

doi:10.1016/j.procs.2018.01.046

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The material of this study is Fuzzy Inference Systems, which is the Neuro-Fuzzy Inference System's unit of the Specialized Intellectual System of Entrant's Abilities Identification (Wang et al, 2019;Peña et al, 2018;Miyata & Omori, 2018).…”

Section: Methodsmentioning

confidence: 99%

Identification of Entrant’s Abilities on the Basis of Sugeno-Type Fuzzy Inference Systems

2022

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In the conditions of effective training in aviation for dispatchers and pilots, it requires the use of infocommunication systems capable of working under conditions of fuzzy uncertainty in real time. The functioning of such systems is based on fuzzy inference systems. However, the development and implementation of these systems requires the creation of fuzzy knowledge bases. Therefore, special attention in this study is paid to the creation of a system of fuzzy inferences and the formation of a fuzzy knowledge base of this system. The result is a lozenge-type fuzzy inference system. The fuzzy knowledge base of the system contains the rules according to which, based on the results of test computer game problems of varying complexity, a conclusion is formed about the applicant’s ability to acquire knowledge and skills in a certain specialty.When developing the rules, both the results of passing different levels of professionally oriented computer test games were taken into account, and the interest of dispatchers and pilots was taken into account. Therefore, the proposed fuzzy rules of the knowledge base of the fuzzy inference system make it possible to assess not only the ability of the controller or pilot to solve certain problems. This dependence of the input dataset on time allows the implementation of a fuzzy inference system of the Sugeno type, using clear input data in the formation of inferences.

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Section: Methodsmentioning

confidence: 99%

Identification of Entrant’s Abilities on the Basis of Sugeno-Type Fuzzy Inference Systems

2022

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“…In the emotional interaction between a human and a machine, the agent plays a key role. In a specific interactive task, emotion is embodied as emotion, and the agent's autonomous perception and emotional expression are important characteristics through which the agent can perceive users' emotions and states, generate appropriate emotional responses through internal learning adjustments, promote emotional communication, and even affect users' emotions [18,19]. Quan et al used the combined cepstrum distance method to identify emotion in speech [20].…”

Section: Related Workmentioning

confidence: 99%

A Mood Semantic Awareness Model for Emotional Interactive Robots

Zhou,

Yu,

Wang

et al. 2024

Sensors

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The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human–machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users’ degree of interest are formulated. By evaluating the degree of interest, the system can determine whether it should transition to a new topic to maintain the user’s interest. This model can also address issues such as overly purposeful responses and rigid emotional expressions in generated replies. Simultaneously, this study explores topic continuation after answering a question, the construction of dialogue rounds, keyword counting, and the creation of a target text similarity matrix for each text in the dialogue dataset. The matrix is normalized, weights are assigned, and the final text score is calculated. In the text with the highest score, the content of dialogue continuation is determined by calculating a subsequent sentence with the highest similarity. This resolves the issue in which the conversational bot fails to continue dialogue on a topic after answering a question, instead waiting for the user to voluntarily provide more information, resulting in topic interruption. As described in the experimental section, both automatic and manual evaluations were conducted to validate the significant improvement in the mood semantic awareness model’s performance in terms of dialogue quality and user experience.

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