Learning Generalized Models by Interrogating Black-Box Autonomous Agents

This paper develops a new approach for estimating the internal model of an autonomous agent that can plan and act, by interrogating it. In this approach, the user may ask an autonomous agent a series of questions, which the agent answers truthfully. Our main contribution is an algorithm that generates an interrogation policy in the form of a sequence of questions to be posed to the agent. Answers to these questions are used to derive a minimal, functionally indistinguishable class of agent models. While asking questions exhaustively for every aspect of the model can be infeasible even for small models, our approach generates questions in a hierarchical fashion to eliminate large classes of models that are inconsistent with the agent. Empirical evaluation of our approach shows that for a class of agents that may use arbitrary black-box transition systems for planning, our approach correctly and efficiently computes STRIPS-like agent models through this interrogation process.