Tibetan Jiu Chess, the national intangible cultural heritage of China, involves complex gameplay with both layout and battle stages, presenting a vast action space. Deep learning effectiveness is often limited by computational and data constraints. To address this, we propose LSAFJiu, a low-resource algorithm based on long sequence action forecasting (LSAF). Its innovation combines an LSAF model, a policy network, and a value network to evaluate game tree leaf nodes. The LSAF model incorporates the ActionFormer for the layout stage and an action-state autoencoding planner for the battle stage, aimed at enhancing decision-making capabilities while reducing resource consumption. The policy and value networks are built on the ResNet50 architecture, utilizing a serial stacked activation function to minimize hardware requirements and improve prediction accuracy. To train the initial policy network, 20000 high-quality game instances were generated by three intelligent programs grounded in human expertise, effectively reducing self-play training time. Experiments conducted on two 2060 GPUs demonstrated that LSAFJiu increased the winning rate by 30.5% while requiring only 2.51% of the simulation time needed by the policy network alone. Additionally, the serial stacked activation function improved prediction accuracy by at least 2%. The overall winning rate of LSAFJiu reached 88% within a reasonable decision time. LSAFJiu is an efficient, low-resource algorithm that offers accurate decision-making for Tibetan Jiu Chess.

Tibetan Jiu chess is a national intangible cultural heritage of People’s Republic of China. The digital game platform has collected some online game data, but due to life habits, most masters and enthusiasts prefer to play offline. To collect offline gameplay data efficiently and quickly for Jiu chess, this paper proposes the intelligent algorithm based on improved You Only Look Once (YOLO) version 8 nano (YOLOv8n) model. Firstly, in order to improve the multi-scale feature fusion ability, the backbone and neck are replaced with co-designing and scaling ConvNets with masked autoencoders (namely ConvNeXt V2) and Gather-and-Distribute (GD) mechanism, respectively. Secondly, to reduce the parameter size of the optimized model, the Layer-Adaptive Magnitude-based Pruning (LAMP) score channel pruning algorithm is proposed. Finally, in order to convert the output of chess piece detection into standard chess manual data, the nearest Euclidean distance information matching algorithm and the game conversion algorithms are also proposed, separately. The experimental data source comes from the video recorded in 2023 National Tibetan Jiu Chess Competition in Qinghai Province. The experimental results show that the accuracy of our proposed model reaches 99.660% compared with other models, and the detection time after pruning is reduced by 5.5 ms. The results confirm that our algorithm can effectively improve and balance the speed and accuracy of chess piece detection.