Engagement Density Control

We present a framework for facilitation robots that regulates imbalanced engagement density in four-participant conversation as the forth participant with proper procedures for obtaining initiatives. Four is the special number in multiparty conversations. The three-participant conversation is the minimum unit where the participants autonomously organize a multiparty conversational situation. The fourth participant is the first person who can objectively observe the conversational situation. In three-participant conversations, social imbalance, in which a participant is left behind in the current conversation, sometimes occurs. In such scenarios, a conversational robot has the potential to objectively observe and control situations as the fourth participant. A four-participant conversational situation, where three participants and a facilitator are participating, is the minimum unit of the facilitation process model.


Four-participant Situation

In this video, the participant C is left behind the current conversation. In such moments, SCHEMA tries to approach the participant C to give him a floor, caring the interaction between A and B simultaneously. First, SCHEMA tries to obtain an initiative, and then asks C to give him a floor.

Cognitive Architecture for Group Process

Based on the requirements and elements of facilitation model, as well as the general concepts of cognitive architectures we reviewed above, we propose a computational architecture for multiparty conversation facilitation robots, namely the SCHEMA Framework. The SCHEMA Framework mainly consists of the following processes: the Perception Process the Procedural Production Process the Language Generation Process. The Perception Process process interprets situations based on visual and auditory information. This process includes Adjacency Recognition, Participation Recognition, Topic Recognition and Question Analysis. Each time the system detects an endpoint of participant’s speech from the automatic speech recognition (ASR) module, it interprets the current situation. This process will be described in Chapter 3 in detail. The Procedural Production Process produces procedural actions to manage a group, referring Goal Management Module. This module is modeled as a reinforcement learning framework (partially observable Markov decision process (POMDP)). The Language Generation Process. is divided into factoid and non-factoid typed answer generation modules. The factoid typed answer generation module refers to structured knowledge databases organized using Semantic Web techniques. The non-factoid typed answer generation module generates the system’s own opinions automatically extracted from a large indefinite number of reviews on the Web. It also has an utterance combination mechanism that combines factoid and non-factoid typed responses to realize the additional phrasing function.



Related Papers

  • Four-Participant Group Conversation: A Facilitation Robot Controlling Engagement Density As the Fourth Participant
    Journal of Computer Speech and Language, 2015. (DOI:10.1016/j.csl.2014.12.001)
    Yoichi Matsuyama, Iwao Akiba, Shinya Fujie and Tetsunori Kobayashi

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