Changqing Ai

About

Changqing Ai is a researcher specializing in artificial intelligence for gaming, with a particular focus on turn-based strategy games. Their major contribution lies in developing novel AI fighting strategies that integrate gradient boosting decision trees (GBDT), logistic regression (LR), and deep learning models. In their most cited work, "GBDT, LR & Deep Learning for Turn-based Strategy Game AI" (2019), Ai proposed a sophisticated approach for generating AI combat tactics in the turn-based fighting game *StoneAge 2* (SA2). This research aimed to enable AI to intelligently select logical skills and targets during gameplay, combining the interpretability of traditional machine learning with the power of deep neural networks. While still early in their career, with the paper garnering 4 citations, Ai's work represents a meaningful step toward more adaptive and human-like AI opponents in gaming environments. Their interdisciplinary approach—merging ensemble methods, regression models, and neural architectures—offers a practical framework for game developers seeking to enhance player experience through smarter non-player characters.

Research Focus

Key Achievements

Top Papers

1. 1
   ₁GBDT, LR & Deep Learning for Turn-based Strategy Game AI

   4 citations · 2019

Key Collaborators

Contact & Links