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.

中文翻译：

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为实现通用认知模型的架构表征类比概念记忆

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突出地位。在本文中，我们探讨了如何将类比处理的计算模型引入这些体系结构中，以便从交互式获得的示例中获取概念。我们为 Soar 提出了一种新的类比概念记忆，以增强其当前的陈述性长期记忆系统。我们将概念学习的问题嵌入到交互式任务学习 (ITL) 和具身语言处理 (ELP) 的更大背景中。我们证明，在所提出的记忆中实施的类比学习方法可以快速学习各种类型的新概念，这些概念不仅在识别环境中的概念方面有用，而且在动作选择方面也很有用。我们的方法已在已实现的认知系统 \textsc{Aileen} 中实例化，并在模拟机器人域上进行了评估。