**7. Conclusions**

High-performance computers have significantly contributed to the development of artificial intelligence in the gaming industry, however, this should not be a reason to ignore the effort of pursuing a perfect evaluation method. This paper presents a Chinese Ludo program. Unlike most chess programs, which depend on high machine performance, the evaluation function in our program is only a linear sum of four factor values. The other contribution of this research is that we innovatively constructed a threat matrix that allows us to easily acquire the threat between any two dice on any two positions. The threat matrix approach can greatly reduce the amount of calculation, which allows the program to run on a 32-bit 80 × 86 SCM with a 100 MHz CPU while supporting a recursive algorithms to search plies. Our results show that, when compared with the real-time computing, our threat matrix approach can reduce computation costs by nearly 90%. Furthermore, the memory consumption is both reduced and relatively stable, which speeds up evaluation by 58%.

**Author Contributions:** F.H. designed the main methodology and the model; M.Z. developed the theoretical framework; All authors provided critical feedback and contributed to the research and analysis of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by the National Natural Science Foundation of China (6217071437; 62072200).

**Conflicts of Interest:** The authors declare no conflict of interest.
