  We present an architecture and system for understanding novel behaviors of an
observed agent. The two main features of our approach are the adoption of
Dennett's intentional stance and analogical reasoning as one of the main
computational mechanisms for understanding unforeseen experiences. Our approach
uses analogy with past experiences to construct hypothetical rationales that
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 analogy in a gas station
environment.
