In this paper we give an entirely domain-independent version of APS that we are implementing in the PEIRCE conceptual graphs workbench. By using conceptual graphs as the "base language" a learning system is capable of refining its own pattern language for evaluating states in the given domain that it finds itself in. In addition to generalizing APS to be domain-independent and CG-based we describe fundamental principles for the development of AI systems based on the structured pattern approach of APS. It is hoped that this effort will lead the way to a more principled, and well-founded approach to the problems of mechanizing machine intelligence.