Generation of Oblivion Models for Communication Agents

Abstract

Robots are being expected to play the role of guessing the oblivion status in humans and intervening appropriately. This paper proposes an oblivion model concerning episode memory for communication agents. First, the subjects were instructed to listen to a certain sentence, immediately after which they were asked to freely describe the content. Moreover, one month after this event, the subjects were once again asked to describe the content, and their status of oblivion (quantity, quality) was investigated using the number of characters and idea units. The oblivion model for the communication agent was generated using the results of this investigation. Based on the results of the experiment it was presumed that, immediately after hearing the sentence it was appropriate to extract the content uniformly from the whole text, whereas after one month it was appropriate to extract the content centered around the main theme of the text. In this paper, the oblivion model corresponding to immediately after the reading (listening) was set as a text that had high score for surprise emotion of the communication agent. Moreover, the oblivion model after one month was set as a text that had the maximum score for the total of all the six emotions of the communication agent taking into account the peak-end rule. Additionally, the efficacy and issues of this model were verified through simulation applying this oblivion model on different texts, and comparison of the results with the oblivion status of the subjects in this study, and automatic summaries.