Asking the Right Questions: Learning Interpretable Action Models Through Query Answering

No. 13: AAAI-21 Technical Tracks 13

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Technical Track on Planning, Routing, and Scheduling

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a user-interpretable vocabulary. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images.

10.1609/aaai.v35i13.17428