RTS game strategy evaluation using extreme learning machine

Abstract

The fundamental game of real-time strategy (RTS) is collecting resources to build an army with military units to kill and destroy enemy units. In this research, an extreme learning machine (ELM) model is proposed for RTS game strategy evaluation. Due to the complicated game rules and numerous playable items, the commonly used tree-based decision models become complex, sometimes even unmanageable. Since complex interactions exist among unit types, the weighted average model usually cannot be well used to compute the combined power of unit groups, which results in misleading unit generation strategy. Fuzzy measures and integrals are often used to handle interactions among attributes, but they cannot handle the predefined unit production sequence which is strictly required in RTS games. In this paper, an ELM model is trained based on real data to obtain the combined power of units in different types. Both the unit interactions and the production sequence can be implicitly and simultaneously handled by this model. Warcraft III battle data from real players are collected and used in our experiments. Experimental results show that ELM is fast and effective in evaluating the unit generation strategies.