Weighted Majority Voting with a Heterogeneous System in the Game of Shogi

被引:1
作者
Takeuchi, Shogo [1 ]
机构
[1] Kochi Univ Technol, Sch Informat, Kami, Japan
来源
2018 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI) | 2018年
关键词
game; majority voting; ensemble method;
D O I
10.1109/TAAI.2018.00035
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we propose a weighted voting method for a heterogeneous game system, which assigns the strength of engines and win probabilities of their positions to the weights for voting. Assigning the strength as the weight solves the problem of weaker engines entering the majority voting. The win probabilities are transformed from the evaluation values by a sigmoid function generated for each engine. Through the sigmoid functions, we can compare the win probabilities between the different engines and resolve the problem of optimistic voting in heterogeneous systems. Optimistic voting, which simply selects the highest-scoring move, may select a suboptimal random move when random players are involved in the game. Finally, we competed the proposed system and other voting systems against a single engine in shogi tournaments and compared the strengths of the systems in shogi. The experimental results confirmed the effectiveness of the proposed method.
引用
收藏
页码:122 / 125
页数:4
相关论文
共 50 条
  • [41] An Intelligent Group Learning Framework for Detecting Common Tomato Diseases Using Simple and Weighted Majority Voting with Deep Learning Models
    Javidan, Seyed Mohamad
    Ampatzidis, Yiannis
    Banakar, Ahmad
    Vakilian, Keyvan Asefpour
    Rahnama, Kamran
    AGRIENGINEERING, 2025, 7 (02):
  • [42] A Generalization of Majority Voting Scheme for Medical Image Detectors
    Toman, Henrietta
    Kovacs, Laszlo
    Jonas, Agnes
    Hajdu, Lajos
    Hajdu, Andras
    HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, PART II, 2011, 6679 : 189 - 196
  • [43] Comparison of Supervector and Majority Voting in Acoustic Scene Identification
    Jiang, Yuechi
    Leung, Frank H. F.
    2018 IEEE 23RD INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP), 2018,
  • [44] Full Characterization of Adaptively Strong Majority Voting in Crowdsourcing
    Boyarskaya, Margarita
    Ipeiroti, Panos
    PROCEEDINGS OF THE ACM COLLECTIVE INTELLIGENCE CONFERENCE, CI 2024, 2024, : 41 - 62
  • [45] New Bounds on the Accuracy of Majority Voting for Multiclass Classification
    Aeeneh, Sina
    Zlatanov, Nikola
    Yu, Jiangshan
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, : 1 - 15
  • [46] Advice is Useful for Game AI: Experiments with Alpha-Beta Search Players in Shogi
    Takeuchi, Shogo
    ADVANCES IN COMPUTER GAMES, ACG 2019, 2020, 12516 : 1 - 10
  • [47] Phishing Detection System Using Extreme Learning Machines with Different Activation Function based on Majority Voting
    Ucar, Murat
    JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 2023, 26 (01): : 401 - 414
  • [48] Spiking Neural P System with weight model of majority voting technique for reliable interactive image segmentation
    Dalvand, Mehran
    Fathi, Abdolhossein
    Kamran, Arezoo
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (12) : 9035 - 9051
  • [49] Spiking Neural P System with weight model of majority voting technique for reliable interactive image segmentation
    Mehran Dalvand
    Abdolhossein Fathi
    Arezoo Kamran
    Neural Computing and Applications, 2023, 35 : 9035 - 9051
  • [50] VOTE ASSIGNMENTS IN WEIGHTED VOTING MECHANISMS
    TONG, ZJ
    KAIN, RY
    IEEE TRANSACTIONS ON COMPUTERS, 1991, 40 (05) : 664 - 667