This paper describes the structure-mapping engine (SME), a program for studying analogical processing . SME has been built to explore Gentner's structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well . We review the structure-mapping theory and describe the design of the engine . We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial . typically bounded by O(N). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work.
In this paper, the authors discuss the challenges faced in developing the knowledge stack for the Companion cognitive architecture and share the tools, representations, and practices they have developed to overcome these challenges. They also outline potential next steps to allow Companion agents to manage their own knowledge more effectively.
