Asking the Right Questions: Learning Interpretable Action Models Through Query Answering
Pulkit Verma, Shashank Rao Marpally, Siddharth Srivastava
Abstract
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.
BibTeX
@inproceedings{aaai2021_askingtherightqu,
title = {Asking the Right Questions: Learning Interpretable Action Models Through Query Answering},
author = {Pulkit Verma and Shashank Rao Marpally and Siddharth Srivastava},
booktitle = {AAAI 2021},
year = {2021}
}