SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games

2026-06-29

AI Summary

This study addresses the challenge of extracting interpretable tactical knowledge from micro-level maneuvers in real-time strategy games, which is hindered by high-dimensional, coupled state-action sequences and opaque decision-making mechanisms. To overcome this, the authors propose SAT-RTS—a novel state-action-tactic analysis pipeline that integrates BK-tree clustering, multidimensional similarity metrics, and rule-driven multi-label extraction to abstract raw behavioral sequences into discrete, human-interpretable tactical labels. The framework further incorporates a hierarchical visualization scheme for attributing tactical decisions, substantially enhancing both the interpretability and computational efficiency of tactical analysis. By enabling fitness landscape visualization, SAT-RTS effectively uncovers latent tactical drivers operating within complex game environments.

Abstract

Efficient tactical knowledge extraction and analysis in real-time strategy (RTS) games micromanagement are constrained by the high-dimensional coupled state-action sequential data and the black-box decision-making process. Current research rarely provides a hierarchical visualization-based attribution analysis from the perspective of data decoupling and abstraction. To facilitate interpretable tactical knowledge extraction and visualization-based analysis in RTS games, a systematic framework named state-action-tactic analysis pipeline (SAT-RTS) is proposed. To decipher the deep-seated drivers of critical decisions in RTS learning systems, this work integrates interpretable visualization with the automated extraction of latent tactical patterns from high-dimensional sequence data. By adapting a cluster-centric BK-tree algorithm and incorporating specialized distance metrics designed to quantify multi-aspect similarities, the proposed framework facilitates robust state-stream abstraction. Furthermore, a rule-based multi-label extraction method is developed to transform unstructured state-action sequences into discrete and interpretable tactical labels, effectively bridging the gap between raw behavioral data and high-level tactical insights. By holistically integrating these computational methods into a hierarchical visualization-based pipeline, the proposed framework effectively addresses the challenges of processing massive real-time data streams while providing fitness landscape visualizations and analytical insights to decipher deep-seated tactical drivers. Comprehensive experiments demonstrate that the proposed SAT-RTS significantly enhances the interpretability and efficiency of tactical analysis in complex RTS environments.

Problem

• real-time strategy games
• tactical knowledge extraction
• state-action sequences
• interpretability
• visualization-based analysis

Innovation

• tactical knowledge extraction
• visualization-based analysis
• state-action abstraction
• BK-tree clustering
• multi-label rule extraction