Abstract I am developing a framework for anomaly reasoning for agents that plan and learn in complex, sometimes unfamiliar domains. Anomaly reasoning encompasses recognizing, interpreting, and reacting to unfamiliar objects or familiar objects appearing in unexpected contexts. As a first approach, I propose an interpretation method in which agents form concepts from perceptions to create new representations for use in planning and decision making. An anomaly reasoning framework will make agents more versatile, facilitate learning transfer by pruning irrelevant features, relate new to known phenomena with appropriate similarity metrics, and guide an agent to aspects of the environment most significant to its goals.