Understanding the Unforeseen via the Intentional Stance

TL;DR

This paper introduces an architecture that employs Dennett's intentional stance and analogical reasoning to understand unforeseen behaviors of agents, enabling more meaningful explanations and better predictions in novel situations.

Contribution

The paper presents a novel system combining intentional stance and analogical reasoning to interpret unforeseen agent behaviors, enhancing explanatory depth and inferential flexibility.

Findings

Analogical reasoning enables better understanding of unforeseen behaviors.

Blending multiple past experiences improves explanation accuracy.

The system can generate meaningful predictions based on explanations.

Abstract

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.