Robot learning by observation based on Bayesian networks and game pattern graphs for human-robot game interactions

Hyunglae Lee, Hyoungnyoun Kim, Kyung Hwa Park, Ji Hyung Park

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

1 Scopus citations

Abstract

This paper describes a new learning by observation algorithm based on Bayesian networks and game pattern graphs. Even with minimal knowledge of a game or human instructions, the robot can learn the game rules by watching human demonstrators repeatedly play the game multiple times. Based on the knowledge acquired from this learning process, represented in Bayesian networks and game pattern graphs, the robot can play games as robustly as humans do. Our learning algorithm for human-robot game interaction is implemented using a teddy bear-like robot and is demonstrated by application to well-known social games, specifically Rock-Paper-Scissors, Muk-Chi-ba and Blackjack.

Original languageEnglish (US)
Title of host publication2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Pages319-325
Number of pages7
DOIs
StatePublished - Dec 1 2008
Externally publishedYes
Event2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS - Nice, France
Duration: Sep 22 2008Sep 26 2008

Publication series

Name2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Other

Other2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
CountryFrance
CityNice
Period9/22/089/26/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Robot learning by observation based on Bayesian networks and game pattern graphs for human-robot game interactions'. Together they form a unique fingerprint.

  • Cite this

    Lee, H., Kim, H., Park, K. H., & Park, J. H. (2008). Robot learning by observation based on Bayesian networks and game pattern graphs for human-robot game interactions. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (pp. 319-325). [4650861] (2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS). https://doi.org/10.1109/IROS.2008.4650861