For intelligent agents to become truly useful in real-world applications, it is necessary to identify, document, and integrate into them the human knowledge used to solve real-world problems. This article describes a methodology for modeling expert problem-solving knowledge that supports ontology import and development, teaching-based agent development, and agent-based problem solving. It provides practical guidance to subject matter experts on expressing how they solve problems using the task reduction paradigm. It identifies the concepts and features to be represented in an ontology; identifies tasks to be represented in a knowledge base; guides rule learning/refinement; supports natural language generation; and is easy to use. The methodology is applicable to a wide variety of domains and has been successfully used in the military domain. This research is part of a larger effort to develop an advanced approach to expert knowledge acquisition based on apprenticeship multi-strategy learning in a mixed-initiative framework.