Scalable Knowledge Acquisition through Memory Organization

Memory organization plays a critical role in knowledge acquisition. An agent must select a small subset of existing knowledge to serve as the basis for new learning; otherwise each problem becomes more complex than the previous. Selecting this subset remains a challenge, however. We propose that existing knowledge be organized in order for a learning agent to achieve its full potential. The SCALE algorithm is presented as a method for knowledge acquisition and organization, and is used to demonstrate both the computational and training benefits of memory organization.