Adaptive behavior generation for conversational robot in human-robot negotiation environment

Miguel Gomez Lopez, Komei Hasegawa, Michita Imai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This study addresses human-robot interactions in a controlled negotiation environment. The aim is to prove that a robot, given its limitations, can win a non-equilibrium based negotiation against a human by convincing him/her. To do so, a behavioral model based on decision trees is proposed, which chooses behavior and action of the robot adaptively depending on the circumstances, robot's intention and human's past response. An experiment under two conditions was conducted: one where the robot was set to play the Desert Survival Situation negotiation game against 10 humans; and one where the robot was compared to other system with the same knowledge about the game but without the behavioral and action generator model. The extracted conclusions were that the robot could win the game in most of the cases, convincing the human. The results also show that its performance is significantly better than the human's and that the other system's robot.

Original languageEnglish
Title of host publicationHAI 2017 - Proceedings of the 5th International Conference on Human Agent Interaction
PublisherAssociation for Computing Machinery, Inc
Pages151-159
Number of pages9
ISBN (Electronic)9781450351133
DOIs
Publication statusPublished - 2017 Oct 17
Event5th International Conference on Human Agent Interaction, HAI 2017 - Bielefeld, Germany
Duration: 2017 Oct 172017 Oct 20

Other

Other5th International Conference on Human Agent Interaction, HAI 2017
Country/TerritoryGermany
CityBielefeld
Period17/10/1717/10/20

Keywords

  • Adaptive model
  • Behavioral model
  • Decision trees
  • Human-robot interaction
  • Negotiation

ASJC Scopus subject areas

  • Human-Computer Interaction

Fingerprint

Dive into the research topics of 'Adaptive behavior generation for conversational robot in human-robot negotiation environment'. Together they form a unique fingerprint.

Cite this