# Society of Mind Meets Real-Time Strategy: A Hierarchical Multi-Agent Framework for Strategic Reasoning Source: [Society of Mind Meets Real-Time Strategy: A Hierarchical Multi-Agent Framework for Strategic Reasoning]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:22:00.744Z Authors: Daechul Ahn, San Kim, Jonghyun Choi Published: 2025-08-08T05:57:12.000Z ## Abstract Large Language Models (LLMs) have recently demonstrated impressive action sequence prediction capabilities but often struggle with dynamic, long-horizon tasks such as real-time strategic games. In a game such as StarCraftII (SC2), agents need to manage resource constraints and adapt to evolving battlefield situations in a partially observable environment. This often overwhelms exisiting LLM-based approaches. To address these challenges, we propose a hierarchical multi-agent framework that employs specialized imitation learning agents under a meta-controller called Strategic Planner (SP). By expert demonstrations, each specialized agent learns a distinctive strategy, such as aerial support or defensive maneuvers, and produces coherent, structured multistep action sequences. The SP then orchestrates these proposals into a single, environmentally adaptive plan that ensures local decisions aligning with long-term strategies. We call this HIMA (Hierarchical Imitation Multi-Agent). We also present TEXTSCII-ALL, a comprehensive SC2 testbed that encompasses all race match combinations in SC2. Our empirical results show that HIMA outperforms state of the arts in strategic clarity, adaptability, and computational efficiency, underscoring the potential of combining specialized imitation modules with meta-level orchestration to develop more robust, general-purpose AI agents. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Global resource management and long-term strategic planning on Melee Flat48 Lv.1 (Very Easy)](): Global resource management and long-term strategic planning; Melee Flat48 Lv.1 (Very Easy); Win Rate: 1. - [Global resource management and long-term strategic planning on Melee Flat32 Lv.1 (Very Easy)](): Global resource management and long-term strategic planning; Melee Flat32 Lv.1 (Very Easy); Win Rate: 94. - [Global resource management and long-term strategic planning on Melee Flat32 Easy Lv.2](): Global resource management and long-term strategic planning; Melee Flat32 Easy Lv.2; Win Rate: 89. - [Global resource management and long-term strategic planning on Melee Flat48 Lv.2 (Easy)](): Global resource management and long-term strategic planning; Melee Flat48 Lv.2 (Easy); Win Rate: 94. - [Global resource management and long-term strategic planning on Melee Flat64 Lv.1 (Very Easy)](): Global resource management and long-term strategic planning; Melee Flat64 Lv.1 (Very Easy); Win Rate: 95. - [Global resource management and long-term strategic planning on Melee Flat64 Easy Lv.2](): Global resource management and long-term strategic planning; Melee Flat64 Easy Lv.2; Win Rate: 89. - [Global resource management and long-term strategic planning on Melee Simple64 Lv.1 (Very Easy)](): Global resource management and long-term strategic planning; Melee Simple64 Lv.1 (Very Easy); Win Rate: 90. - [Global resource management and long-term strategic planning on Melee Simple64 Lv.2 (Easy)](): Global resource management and long-term strategic planning; Melee Simple64 Lv.2 (Easy); Win Rate: 85. - [Multi-agent Micromanagement on SMAC 3m](): Real-time micro-management; SMAC 3m; Win Rate: 66. - [Real-time micro-management on SMAC 8m](): Real-time micro-management; SMAC 8m; Win Rate: 54. - [Real-time micro-management on SMAC 25m](): Real-time micro-management; SMAC 25m; Win Rate: 10. ## Original sources and implementation [Original paper]() [PDF]()