General Language Evolution in General Game Playing

被引:2
|
作者
Chitizadeh, Armin [1 ]
Thielscher, Michael [1 ]
机构
[1] UNSW Sydney, Sydney, NSW 2052, Australia
关键词
General game playing with incomplete information; Language learning; Multi-agent coordination; Fictitious play; Evolutionary computing;
D O I
10.1007/978-3-030-03991-2_5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
General Game Playing (GGP) is concerned with the development of programs capable of expertly playing a game by just receiving its rules and without human intervention. Its standard Game Description Language (GDL) has been extended so as to include incomplete information games. The extended version is named as GDL-II. Different algorithms were recommended to play games in GDL-II, however, none of them can solve coordination games properly. One reason for this shortcoming is their inability to generate the necessary coordination language. On the other side, most existing language evolution techniques focus on generating a common language without considering its generality or its use for problem solving. In this paper, we will extend GGP with language evolution to develop a general language generation technique. The new technique can be combined with GGP algorithms for incomplete-information games and assist players in automatically generating a common language to solve cooperation problems.
引用
收藏
页码:51 / 64
页数:14
相关论文
共 50 条
  • [31] General game playing: Overview of the AAAI competition
    Genesereth, M
    Love, N
    Pell, B
    AI MAGAZINE, 2005, 26 (02) : 62 - 72
  • [32] A Neuroevolution Approach to General Atari Game Playing
    Hausknecht, Matthew
    Lehman, Joel
    Miikkulainen, Risto
    Stone, Peter
    IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, 2014, 6 (04) : 355 - 366
  • [33] Exploring a Learning Architecture for General Game Playing
    Gunawan, Alvaro
    Ruan, Ji
    Thielscher, Michael
    Narayanan, Ajit
    AI 2020: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 12576 : 294 - 306
  • [34] Contextual Decision Making in General Game Playing
    Sheng, Xinxin
    Thuente, David
    2011 23RD IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2011), 2011, : 679 - 684
  • [35] General Game Playing in AI Research and Education
    Thielscher, Michael
    KI 2011: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2011, 7006 : 26 - 37
  • [36] General game-playing and reinforcement learning
    Levinson, R
    COMPUTATIONAL INTELLIGENCE, 1996, 12 (01) : 155 - 176
  • [37] The 2014 General Video Game Playing Competition
    Perez-Liebana, Diego
    Samothrakis, Spyridon
    Togelius, Julian
    Schaul, Tom
    Lucas, Simon M.
    Couetoux, Adrien
    Lee, Jerry
    Lim, Chong-U
    Thompson, Tommy
    IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, 2016, 8 (03) : 229 - 243
  • [38] Deep Reinforcement Learning for General Game Playing
    Goldwaser, Adrian
    Thielscher, Michael
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 1701 - 1708
  • [39] The Axiom General Purpose Game Playing System
    Schmidt, Gregory
    IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, 2014, 6 (04) : 332 - 342
  • [40] General Game Playing: An Overview and Open Problems
    Sharma, Shiven
    Kobti, Ziad
    Goodwin, Scott D.
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTING, ENGINEERING AND INFORMATION, 2009, : 257 - 260