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首页> 外文会议>Eleventh Conference on Computer-Generated Forces and Behavior Representation, May 7-9, 2002, Orlando, Florida >Can a Composite Agent Be Used to Implement a Recognition-Primed Decision Model?
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Can a Composite Agent Be Used to Implement a Recognition-Primed Decision Model?

机译：可以使用复合代理来实现以识别为主体的决策模型吗？

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摘要

Legacy constructive military simulation systems such as the Corps Battle Simulation (CBS) and the Joint Theater Level Simulation (JTLS) have simple models of military commanders and their decision-making process. It would be useful to the military community to advance the robustness and the realism of these decision models. To improve human decisions in a simulation we need better models of human decision-making. The Recognition-Primed Decision (RPD) model was developed to explain the mental process that decision makers, especially experienced ones such as senior military commanders, go through to arrive at a decision. Several efforts are ongoing to try to produce a computational model of RPD. These efforts are centered on rule-based, neural network, and fuzzy logic approaches. This paper analyzes a different approach, using a composite agent, to implement the RPD model. A composite agent uses multi-agent system simulation technology to implement various cognitive processes of a single entity or agent. It is this author's contention that a composite agent's decision-making method closely matches that described by the RPD model. This close match is expected to produce a better implementation of the RPD model.
机译：传统的建设性军事模拟系统，如“兵团战斗模拟”（CBS）和“联合战区级模拟”（JTLS），具有简单的军事指挥官模型及其决策过程。对于军事界来说，提高这些决策模型的鲁棒性和现实性将是有用的。为了改善仿真中的人为决策，我们需要更好的人为决策模型。开发以识别为基础的决策（RPD）模型来解释决策者，尤其是资深军事指挥官等经验丰富的决策者所经历的心理过程。正在进行一些努力来尝试生成RPD的计算模型。这些工作集中在基于规则的神经网络和模糊逻辑方法上。本文分析了使用复合代理来实现RPD模型的另一种方法。复合主体使用多主体系统仿真技术来实现单个实体或主体的各种认知过程。作者的观点是，复合主体的决策方法与RPD模型描述的决策方法非常匹配。这种紧密匹配有望更好地实现RPD模型。

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