Knowledge-based agents in a command and control simulation

K. Lehnert, M. Sullivan
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Abstract

To facilitate research into multi-agent planning issues, we have developed the Command and Control Testbed Using Simulation (CACTUS) and the Situation-based Autonomous Reasoner in a GBB Environment (SARGE). CACTUS is a simulation of combat operations for platoon-sized units in battalion-level engagements. We model in detail those command and control problems impacting the way planning is done by a formation commander. These include incomplete and uncertain information, imperfect control, differing agent characteristics, and limited inter-unit communications.The testbed originally required the user to input specific movement and firing instructions for each unit simulated. An interface to the simulation permits the integration of automated planners in place of user input. The SARGE commander was built to relieve the user of the need to provide detailed instructions at the simulation system level. Units with SARGE commanders take goal level instructions and SARGE then explictly reasons about plans to achieve those goals and submits the instructions necessary to implement those plans in CACTUS. SARGE uses the blackboard model of reasoning as implemented in the Generic Blackboard (GBB) tool. User access to the knowledge affecting SARGE behavior allows us to assess the impact of different tactical and doctrinal schemes on planning at higher levels of command.The SARGE and CACTUS systems provide the foundations for developing automated planners for multiple unit formations by raising the level of abstraction for inputting instruction to subordinate units. We will use this incremental approach to address command and control issues at progressively higher levels in the chain of command and to develop, refine, and evaluate artificial intelligence techniques to integrate human and machine capabilities. Our ultimate goal is to collect applicable techniques into a system supporting rapid prototyping of tactical decision aids. SARGE and CACTUS are vital steps in this process.
命令与控制仿真中的知识代理
为了促进对多智能体规划问题的研究,我们开发了使用仿真的指挥与控制试验台(CACTUS)和GBB环境中基于情境的自主推理器(SARGE)。CACTUS是在营级交战中为排大小的单位进行作战操作的模拟。我们对那些影响编队指挥官规划方式的指挥和控制问题进行了详细建模。这些包括不完整和不确定的信息,不完善的控制,不同的代理特性,有限的单位间通信。试验台最初要求用户输入特定的运动和射击指令,以模拟每个单元。模拟的接口允许集成自动化计划器来代替用户输入。SARGE指挥系统的建立是为了减轻用户在模拟系统级别提供详细说明的需要。拥有SARGE指挥官的单位接受目标级别的指令,然后SARGE明确说明实现这些目标的计划,并在CACTUS中提交实施这些计划所需的指令。SARGE使用在通用黑板(GBB)工具中实现的黑板推理模型。用户对影响SARGE行为的知识的访问使我们能够评估不同战术和理论方案对更高级别指挥规划的影响。SARGE和CACTUS系统通过提高向下级单位输入指令的抽象级别,为开发多单位编队的自动化计划器提供了基础。我们将使用这种增量方法在指挥链中逐步解决更高层次的指挥和控制问题,并开发、改进和评估人工智能技术,以整合人与机器的能力。我们的最终目标是将适用的技术收集到一个支持战术决策辅助快速原型的系统中。SARGE和CACTUS是这个过程中至关重要的步骤。
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