Publicado en 3C TIC – Volume 12 Issue 1 (Ed. 42)
To broaden the application area of the cognitive-emotional interaction model for robots. In this paper, an algorithmic model for the English translation of environmental articles based on a cognitive-emotional interaction model for robots is used to model the process of emotion generation using reinforcement learning. Similarly, positivity and empathy are used to quantify the reward function for emotional state assessment, and the optimal emotional strategy selection is derived based on the utility function. In the process of article translation by the robot, Lagrangian factors are introduced to make the translation probability maximum process transformed into the process of obtaining the highest value of the auxiliary function at a random state. Finally, the effectiveness of the robot's cognitive-emotional interaction model in the English translation of environmental protection articles is verified by the Chinese-English parallel question-and-answer dataset. The experimental results demonstrate that this model can not only be used for the English translation of environmental protection articles but also can give the corresponding English translation work similar to human emotions, which can better help people understand the meaning of English. It also provides a basis and direction for the subsequent in-depth application of the robot cognitive-emotional interaction model in various fields.
KeywordsRobot cognitive model; Emotional interaction model; Optimal emotional strategy; Emotional state assessment reward function; Reinforcement learning model
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