Dynamic Emotional Transition Sampling and Emotional Guidance of Individuals Based on Conversation

Abstract

Emotion is the vital path to achieving strong artificial intelligence. Therefore, it is significant to study the emotional guiding and controlling theory to enhance system intelligence. Conversational datasets in social media contain useful information, including individuals exhibiting continuously changing emotion states in response to external stimuli, and are the foundation for research on artificial emotions. We define dialog-based emotional guidance in reinforcement learning to research the emotional guidance of the discrete emotion model. This article proposes three strategies to obtain the optimal policy: 1) given the current emotional transition matrix, use the emotional Markov decision process (E-MDP) algorithm to calculate the optimal stimulus policy for each target emotion; 2) given the emotional transition sequences, use the emotional Monte Carlo algorithm to calculate the optimal stimulus policy; and 3) given the emotional transition sequences, use the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning algorithm to calculate the optimal stimulus policy. Besides, we improve the Markov chain Monte Carlo algorithm to sample emotional transition sequences and design a metric to evaluate the effectiveness of policies. Experimental results on three datasets show that these methods can more effectively guide emotion than traditional methods. Particularly, E-MDP achieves the best results while others can be more widely used in real-world scenarios.