基于神经遗传算法的Mamdani模糊推理系统在战术空战决策支持系统中的应用

DungeonBench：龙与地下城战斗中规则丰富的战术推理基准

DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat

本研究推出DungeonBench基准，用于测试龙与地下城战斗中的规则丰富战术推理，含单次遭遇和关联遭遇两个赛道，评估发现前沿语言模型策略在关联遭遇中存在资源管理等问题

DRG-MAPPO：用于协同空战的分层动态角色图多智能体强化学习

DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat

提出DRG-MAPPO框架，结合图关系建模与动态角色分配，实现协同空战中的战术协同，达到87%的胜率。

战术战伤救护的教义基础视觉问答数据集

A doctrine-grounded visual question answering dataset for Tactical Combat Casualty Care

针对战术战伤救护（TC3）场景，构建了基于教义的视觉问答数据集TC3-VQA，含581个项目、1860个问题，用于训练和评估视觉-语言模型的干预识别、教义回忆及弃权（不执行）能力。

Research on Autonomous Maneuvering Decision of UCAV based on Approximate Dynamic Programming

Unmanned aircraft systems can perform some more dangerous and difficult missions than manned aircraft systems. In some highly complicated and changeable tasks, such as air combat, the maneuvering decision mechanism is required to sense the combat situation accurately and make the optimal strategy in real-time. This paper presents a formulation of a 3-D one-on-one air combat maneuvering problem and an approximate dynamic programming approach for computing an optimal policy on autonomous maneuvering decision making. The aircraft learns combat strategies in a Reinforcement Leaning method, while s…

分层多智能体强化学习在真实空战中的协调策略

Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning

针对真实空战中的态势感知与非线性动力学挑战，提出分层多智能体强化学习框架，结合异构动力学、课程学习与联赛对战，提升缠斗场景的学习效率与作战性能。

决策支持系统的并发模糊-神经网络方法

A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems

决策是一个在多准则目标的复杂问题中进行选择的过程。在过去的几年里，人们越来越认识到软计算技术是智能系统的概念、设计和利用的基础。工程师和科学家们已经做了一些工作，应用智能技术和数学从不精确的信息中获得最佳决策。本文提出了一种结合无监督学习和监督学习技术的并行模糊神经网络方法来开发战术空战决策支持系统（TACDSS）。实验结果清楚地证明了所提出的技术的效率。