实现共同认知模型的建筑物的类比概念记忆特征
Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition
摘要：
实现通用认知模型的架构- Soar、ACT-R和Sigma -在认知建模和设计复杂智能代理的研究中占有重要地位。在本文中，我们将探讨如何将类比处理的计算模型引入这些架构中，以使从交互式获得的示例中获取概念。我们提出了一个新的类比概念记忆的腾飞，增强其目前的陈述性长期记忆系统。我们框架的概念学习的问题嵌入在更大的背景下的互动任务学习（ITL）和体现语言处理（ELP）。我们证明，在建议的内存中实现的类比学习方法可以快速学习各种类型的新概念，不仅在识别环境中的概念，而且在动作选择中是有用的。我们的方法已实例化的认知系统\textsc{Aileen}和模拟机器人域上进行评估。
英文摘要：
Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable concept acquisition from examples obtained interactively. We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We frame the problem of concept learning as embedded within the larger context of interactive task learning (ITL) and embodied language processing (ELP). We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts that are useful not only in recognition of a concept in the environment but also in action selection. Our approach has been instantiated in an implemented cognitive system \textsc{Aileen} and evaluated on a simulated robotic domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
