  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.
