Tom Williams

AAAI Conference 2020 Conference Paper

Dempster-Shafer Theoretic Learning of Indirect Speech Act Comprehension Norms

• Ruchen Wen
• Mohammed Aun Siddiqui
• Tom Williams

For robots to successfully operate as members of humanrobot teams, it is crucial for robots to correctly understand the intentions of their human teammates. This task is particularly difficult due to human sociocultural norms: for reasons of social courtesy (e. g. , politeness), people rarely express their intentions directly, instead typically employing polite utterance forms such as Indirect Speech Acts (ISAs). It is thus critical for robots to be capable of inferring the intentions behind their teammates’ utterances based on both their interaction context (including, e. g. , social roles) and their knowledge of the sociocultural norms that are applicable within that context. This work builds off of previous research on understanding and generation of ISAs using Dempster-Shafer Theoretic Uncertain Logic, by showing how other recent work in Dempster-Shafer Theoretic rule learning can be used to learn appropriate uncertainty intervals for robots’ representations of sociocultural politeness norms.

AAMAS Conference 2017 Conference Paper

A Tale of Two Architectures: A Dual-Citizenship Integration of Natural Language and the Cognitive Map

• Tom Williams
• Collin Johnson
• Matthias Scheutz
• Benjamin Kuipers

Vulcan and DIARC are two robot architectures with very different capabilities: Vulcan uses rich spatial representations to facilitate navigation capabilities in real-world, campus-like environments, while DIARC uses high-level cognitive representations to facilitate human-like tasking through natural language. In this work, we show how the integration of Vulcan and DIARC enables not only the capabilities of the two individual architectures, but new synergistic capabilities as well, as each architecture leverages the strengths of the other. This integration presents interesting challenges, as DIARC and Vulcan are implemented in distinct multi-agent system middlewares. Accordingly, a second major contribution of this paper is the Vulcan-ADE Development Environment (VADE): a novel multi-agent system framework comprised of both (1) software agents belonging to a single robot architecture and implemented in a single multi-agent system middleware, and (2) “Dual-Citizen” agents that belong to both robot architectures and that use elements of both multi-agent system middlewares. As one example application, we demonstrate the implementation of the new joint architecture and novel multi-agent system framework on a robotic wheelchair, and show how this integration advances the state-of-the-art for NL-enabled wheelchairs.

AAAI Conference 2016 Conference Paper

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

• Tom Williams
• Matthias Scheutz

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.

AAAI Conference 2016 Conference Paper

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

• Tom Williams

AAAI Conference 2015 Conference Paper

Going Beyond Literal Command-Based Instructions: Extending Robotic Natural Language Interaction Capabilities

• Tom Williams
• Gordon Briggs
• Bradley Oosterveld
• Matthias Scheutz

The ultimate goal of human natural language interaction is to communicate intentions. However, these intentions are often not directly derivable from the semantics of an utterance (e. g. , when linguistic modulations are employed to convey politeness, respect, and social standing). Robotic architectures with simple command-based natural language capabilities are thus not equipped to handle more liberal, yet natural uses of linguistic communicative exchanges. In this paper, we propose novel mechanisms for inferring intentions from utterances and generating clarification requests that will allow robots to cope with a much wider range of task-based natural language interactions. We demonstrate the potential of these inference algorithms for natural humanrobot interactions by running them as part of an integrated cognitive robotic architecture on a mobile robot in a dialoguebased instruction task.