Applying psychology of persuasion to conversational agents through reinforcement learning: An exploratory study

Patrizia Catellani, Valentina Carfora, Di Massimo Francesca, Marco Piastra

Risultato della ricerca: Working paper

Abstract

This study is set in the framework of taskoriented conversational agents in which dialogue management is obtained via reinforcement Learning. The aim is to explore the possibility to overcome the typical end-to-end training approach through the integration of a quantitative model developed in the field of persuasion psychology. Such integration is expected to accelerate the training phase and improve the quality of the dialogue obtained. In this way, the resulting agent would take advantage of some subtle psychological aspects of the interaction that would be difficult to elicit via end-to-end training. We propose a theoretical architecture in which the psychological model above is translated into a probabilistic predictor and then integrated in the reinforcement learning process, intended in its partially observable variant. The experimental validation of the architecture proposed is currently ongoing.
Lingua originaleEnglish
EditoreCEUR-WS.org
Numero di pagine6
Stato di pubblicazionePubblicato - 2019

Keywords

  • conversational agents

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