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The synthetic teammate project

机译:综合队友项目

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摘要

The main objective of the Synthetic Teammate project is to develop language and task enabled synthetic agents capable of being integrated into team training simulations. To achieve this goal, the agents must be able to closely match human behavior. The initial application for the synthetic teammate research is creation of an agent able to perform the functions of a pilot for an Unmanned Aerial Vehicle (UAV) simulation as part of a three-person team. The agent, or synthetic teammate, is being developed in the ACT-R cognitive architecture. The major components include: language comprehension and generation, dialog management, agent-environment interaction, and situation assessment. Initial empirical results suggest that the agent-environment interaction is a good approximation to human behavior in the UAV environment, and we are planning further empirical tests of the synthetic teammate operating with human teammates. This paper covers the project's modeling approach, challenges faced, progress made toward an integrated synthetic teammate, and lessons learned during development.
机译:合成队友项目的主要目标是开发能够集成到团队训练模拟中的语言和具有任务功能的合成代理。为了实现此目标,代理必须能够紧密匹配人类行为。综合队友研究的最初应用是创建一个代理,该代理能够作为三人团队的一部分执行无人驾驶飞机(UAV)模拟飞行员的功能。该代理或合成队友正在ACT-R认知架构中开发。主要组件包括:语言理解和生成,对话管理,代理与环境的交互以及状况评估。初步的经验结果表明,代理人与环境之间的相互作用非常接近于无人机环境中的人类行为,并且我们正在计划对与人类队友一起工作的合成队友进行进一步的经验测试。本文介绍了该项目的建模方法,面临的挑战,在集成综合队友方面取得的进展以及在开发过程中获得的经验教训。

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