An explanatory reasoning framework for embodied agents

Our interest is in developing embodied cognitive systems. In the majority of work on cognitive modeling, the focus is on generating models that can perform specific tasks in order to understand specific reasoning processes. This approach has traditionally been exceptionally successful at accomplishing its goal. The approach encounters limitations, however, when the cognitive models are going to be used in an embodied way (e.g., on a robot). Namely, the models are too narrow to operate in the real world due to its unpredictability. In this paper, we argue that one key way for cognitive agents to better operate in real-world environments is to be able to identify and explain unexpected situations in the world; in other words, to perform explanatory reasoning. In this paper, we introduce a framework for explanatory reasoning that describes a way for cognitive agents to achieve this capability.