Robot Learning by Observation based on Bayesian Networks and Game Pattern Graphs for Human-Robot Game Interactions

Authors
Lee, HyunglaeKim, HyoungnyounPark, Kyung-HwaPark, Ji-Hyung
Issue Date
2008
Publisher
IEEE
Citation
IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.319 - 325
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.
URI
https://pubs.kist.re.kr/handle/201004/116094
DOI
10.1109/IROS.2008.4650861
Appears in Collections:
KIST Conference Paper > 2008
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE