Rahul Dubey

Papers

Total Citations

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About

Rahul Dubey is a researcher in artificial intelligence and computational game design, with a focus on real-time strategy (RTS) games. His work explores the intersection of machine learning, optimization, and autonomous decision-making in dynamic environments. Dubey’s most cited paper, “Comparing Three Approaches to Micro in RTS Games” (2019), systematically evaluates three distinct techniques for micromanaging units—meta-search using genetic algorithms, parameterized control algorithms, and pure potential fields—offering a clear framework for understanding trade-offs between interpretability, adaptability, and performance. This comparative study has garnered 4 citations, serving as a foundational reference for researchers seeking to balance human-specified strategies with automated optimization. Dubey’s contributions are particularly valuable for students and practitioners in game AI, robotics, and multi-agent systems, as his work bridges heuristic control and evolutionary computation. By demystifying complex micromanagement challenges, he provides actionable insights for developing more responsive and intelligent agents in real-time settings. His research continues to influence the design of adaptive systems where rapid, tactical decisions are critical.