A Framework for Resolving Open-World Referential Expressions in Distributed Heterogeneous Knowledge Bases

Tom Williams and Matthias Scheutz

30th AAAI Conference on Artificial Intelligence

We present a domain-independent approach to reference resolution that allows a robotic or virtual agent to resolve references to entities (e.g., objects and locations) found in open worlds when the information needed to resolve such references is distributed among multiple heterogeneous knowledge-bases in its architecture. An agent using this approach can combine information from multiple sources without the computational bottleneck associated with centralized knowledge bases. The proposed approach also facilitates "lazy constraint evaluation", i.e., verifying properties of the referent through different modalities only when the information is needed. After presenting a framework specifying the interfaces by which a reference resolution algorithm can request information from an integrated robot architecture's distributed knowledge bases, we present an algorithm for performing open-world reference resolution within that framework, evaluate the algorithm's performance, and demonstrate its behavior on a simulated robot.