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Journal of Shenyang Aerospace University >

2025 , Vol. 42 >Issue 4: 59 - 67

DOI: https://doi.org/10.3969/j.issn.2095-1248.2025.04.009

Information Science and Engineering

Optimization of MCTS algorithm for Tibetan Jiu Chess by incorporating prior knowledge

  • Yajie WANG ,
  • Feng GU ,
  • Song LIU ,
  • Jingyi YANG ,
  • Shipeng WANG
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  • Engineering Training Center，Shenyang Aerospace University，Shenyang 110136，China

Received date: 2024-12-24

  Revised date: 2025-01-09

  Accepted date: 2025-01-12

  Online published: 2025-08-19

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Abstract

Tibetan Jiu Chess， a traditional folk chess game， is a complete information game that carries the profound Tibetan civilization and splendid culture. In view of the complexity of the rule system and the diversity of the game changes， the traditional game search algorithm is unable to cope with the vast game board and complex strategies. In order to improve the intelligence level of Tibetan Jiu Chess， a Monte Carlo tree search （MCTS） algorithm optimization strategy incorporating prior knowledge was proposed. The strategy was based on deep reinforcement learning in the key phases of layout planning and move strategy，and the strategy selection optimization function and evaluation function were designed by integrating the prior knowledge of domain experts. The search process of MCTS was efficiently guided by functions，and the best model for high-quality tessellation could be trained. Experimental results show that the improved MCTS algorithm achieves significant performance in the game.

Key words： Tibetan Jiu Chess; prior knowledge; Monte Carlo tree search; deep reinforcement learning; strategy selection optimization function; evaluation function

Cite this article

Yajie WANG , Feng GU , Song LIU , Jingyi YANG , Shipeng WANG . Optimization of MCTS algorithm for Tibetan Jiu Chess by incorporating prior knowledge[J]. Journal of Shenyang Aerospace University, 2025 , 42(4) : 59 -67 . DOI: 10.3969/j.issn.2095-1248.2025.04.009

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