SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games
arXiv:2606.30090v1 Announce Type: new 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) i
The increasing complexity and adoption of AI in gaming and broader decision-making systems necessitate more interpretable and efficient methods for understanding AI's tactical processes.
Improving AI's ability to extract and visualize tactical knowledge can lead to advances in AI-driven automation, strategic planning, and understanding complex system behaviors beyond gaming.
This framework offers a systematic way to deconstruct and analyze AI decision-making in high-dimensional environments, potentially enabling more robust and explainable AI agents.
- · AI researchers
- · Game development industry
- · Defence tech
- · Data analysis software companies
- · Black-box AI systems
- · Traditional manual strategy analysis methods
Tools like SAT-RTS will lead to more transparent and efficient development of AI agents capable of complex tactical reasoning.
The methodologies could be adapted for military simulations or autonomous system control, improving real-time decision support and human-AI teaming.
Generalized tactical knowledge extraction might accelerate the development of truly autonomous AI agents capable of strategic reasoning across diverse, complex operational domains.
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Read at arXiv cs.AI