Validating and Fine-Tuning of Game Evaluation Functions Using Endgame Databases

Abstract

The evaluation function and search algorithm are the two main components of almost all game playing programs. A good evaluation function is carefully designed to assess a position by considering the location and the material value of all pieces on board. Normally, an evaluation function f is manually designed, which requires a large amount of expert knowledge. Usually, f must be able to evaluate any position. Theoretically, a huge table that stores all the pre-computed scores for every position can perfectly represent any position. However, it is space-efficient to encode f, which is far from being perfect. On the other hand, endgame databases provide game theoretical values for all legal positions when the total number of pieces remains is small, say within 5 or 6 for Chinese dark chess (CDC). However, only a selected number of endgame databases are available. Furthermore, the size of an endgame database is huge, e.g., from megabytes to gigabytes. We construct a scheme to use the information from endgame databases to validate and fine-tune a manually designed evaluation function. Our method abstracts critical information from a huge database and then validates f on positions when they are contained in an endgame database. Using this information, we then discover meta knowledge to fine-tune and revise f so that f better evaluates a position even when f is fed with positions containing many pieces. Experimental results show that our approach is successful.