Learning to win games in a few examples: Using game-theory and demonstrations to learn the win conditions of a connect four game

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

1 Scopus citations

Abstract

Teaching robots new skills using minimal time and effort has long been a goal of artificial intelligence. This paper investigates the use of game theoretic representations to represent interactive games and learn their win conditions by interacting with a person. Game theory provides the formal underpinnings needed to represent the structure of a game including the goal conditions. Learning by demonstration, has long sought to leverage a robot’s interactions with a person to foster learning. This paper combines these two approaches allowing a robot to learn a game-theoretic representation by demonstration. This paper demonstrates how a robot can be taught the win conditions for the game Connect Four using a single demonstration and a few trial examples with a question and answer session led by the robot. Our results demonstrate that the robot can learn any win condition for the standard rules of the Connect Four game, after demonstration by a human, irrespective of the color or size of the board and the chips. Moreover, if the human demonstrates a variation of the win conditions, we show that the robot can learn the respective changed win condition.

Original languageEnglish (US)
Title of host publicationSocial Robotics - 10th International Conference, ICSR 2018, Proceedings
EditorsElizabeth Broadbent, Shuzhi Sam Ge, Miguel A. Salichs, Álvaro Castro-González, Hongsheng He, John-John Cabibihan, Alan R. Wagner
PublisherSpringer Verlag
Pages349-358
Number of pages10
ISBN (Print)9783030052034
DOIs
StatePublished - Jan 1 2018
Event10th International Conference on Social Robotics, ICSR 2018 - Qingdao, China
Duration: Nov 28 2018Nov 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11357 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Social Robotics, ICSR 2018
CountryChina
CityQingdao
Period11/28/1811/30/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Ayub, A., & Wagner, A. R. (2018). Learning to win games in a few examples: Using game-theory and demonstrations to learn the win conditions of a connect four game. In E. Broadbent, S. S. Ge, M. A. Salichs, Á. Castro-González, H. He, J-J. Cabibihan, & A. R. Wagner (Eds.), Social Robotics - 10th International Conference, ICSR 2018, Proceedings (pp. 349-358). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11357 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-05204-1_34