Learning Interrogation Strategies while Considering Deceptions in Detective Interactive Stories

Authors

  • Guan-Yi Chen National Tsing Hua University
  • Edward Kao National Tsing Hua University
  • Von-Wun Soo National Tsing Hua University

Keywords:

Reinforcement Learning, Interactive Drama and Story Generation, Emotion

Abstract

The strategies for interactive characters to select appropriate dialogues remain as an open issue in related research areas. In this paper we propose an approach based on reinforcement learning to learn the strategy of interrogation dialogue from one virtual agent toward another. The emotion variation of the suspect agent is modeled with a hazard function, and the detective agent must learn its interrogation strategies based on the emotion state of the suspect agent. The reinforcement learning reward schemes are evaluated to choose the proper reward in the dialogue. Our contribution is twofold. Firstly, we proposed a new framework of reinforcement learning to model dialogue strategies. Secondly, background knowledge and emotion states of agents are brought into the dialogue strategies. The resulted dialogue strategy in our experiment is sensitive in detecting lies from the suspect, and with it the interrogator may receive more correct answer.

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Published

2021-06-30

How to Cite

Chen, G.-Y., Kao, E., & Soo, V.-W. (2021). Learning Interrogation Strategies while Considering Deceptions in Detective Interactive Stories. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 9(1), 114-120. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/12668