Understanding the Unforeseen via the Intentional Stance

We present an architecture and system for understanding novel behaviors of an observed agent. Our approach uses analogy with past experiences to construct hypothetical rationales that attempt to explain the behavior of an observed agent. Moreover, we view analogies as partial; thus multiple past experiences can be blended to analogically explain an unforeseen event, leading to greater inferential flexibility. We argue that this approach results in more meaningful explanations of observed behavior than approaches based on surface-level comparisons. A key advantage of behavior explanation over classification is the ability to i) take appropriate responses based on reasoning and ii) make non-trivial predictions that allow for the verification of the hypothesized explanation. We provide a simple use case to demonstrate novel experience understanding through analogies in a gas station environment.