Williams, Tom

Architectural Mechanisms for Situated Natural Language Understanding in Uncertain and Open Worlds

AAAI Conferences

As natural language capable robots and other agents become more commonplace, the ability for these agents to understand truly natural human speech is becoming increasingly important. What is more, these agents must be able to understand truly natural human speech in realistic scenarios, in which an agent may not have full certainty in its knowledge of its environment, and in which an agent may not have full knowledge of the entities contained in its environment. As such, I am interested in developing architectural mechanisms which will allow robots to understand natural language in uncertain and open-worlds. My work towards this goal has primarily focused on two problems: (1) reference resolution, and (2) pragmatic reasoning.

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

AAAI Conferences

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 specifying the interfaces by which a reference resolution algorithm can request information from distributed knowledge bases, we present an algorithm for performing open-world reference resolution within that framework, analyze the algorithm’s performance, and demonstrate its behavior on a simulated robot.