Asking the Right Questions: Learning Interpretable Action Models Through Query Answering
This addresses the challenge of understanding and interpreting complex autonomous systems for researchers and practitioners in AI and robotics, representing a new paradigm rather than an incremental improvement.
The paper tackles the problem of estimating interpretable, relational models of black-box autonomous agents that can plan and act, by introducing a new paradigm using a minimal query interface and a hierarchical querying algorithm, resulting in correct and scalable estimation for a wide class of agents, including those with image-based states.
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 minimal query interface with the agent, and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a vocabulary provided by the user. 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.