Situated Natural Language Interaction in Uncertain and Open Worlds
intelligent agents become integrated into our society, it becomes increasingly important
for them to be capable of engaging in natural, human-like human-agent interactions. A
key aspect of such interactions is the ability to engage in pragmatically appropriate
natural language dialogues. That is, intelligent agents must be able to understand and
generate natural language expressions ... read morein a way that is sensitive to their current
environmental context, social context, and dialogue state. This problem is especially
difficult in the uncertain and open worlds common to typical human-robot interaction
scenarios, in which a robot cannot be expected to have perfect or complete knowledge of
its environment. What is more, many of the approaches that have been developed to
facilitate human-robot dialogues are tailored to specific knowledge representation
schemes or particular domains of information that prevent them from being generally
applicable across robot architectures or across application domains. To address these
concerns, I have developed a set of algorithms for understanding and generating natural
language in uncertain and open worlds, and a set of general frameworks and architectural
mechanisms that allow these algorithms to be agnostic to representational format and
application domain whenever possible. The algorithms and architectural mechanisms
presented in this dissertation represent an interdisciplinary approach to artificial
intelligence, in which cognitive science is drawn upon to provide theoretical frameworks
(e.g., Speech Act Theory, the Givenness Hierarchy), and cognitive models (e.g. the
Incremental Algorithm), and in which computer science is drawn upon to provide
computational frameworks (e.g., Multi-Agent Systems, Integrated Robot Architectures) and
techniques (Dempster-Shafer Theory, logical inference, search). In this dissertation, I
demonstrate how these algorithms and architectural mechanisms can be integrated into a
single natural language processing pipeline within an integrated robot architecture.
What is more, I show how this integrated system extends the state of the art in domains
such as natural language enabled wheelchairs when implemented on robot
Thesis (Ph.D.)--Tufts University, 2017.
Submitted to the Dept. of Computer Science.
Advisor: Matthias Scheutz.
Committee: Anselm Blumer, Jan de Ruiter, and Candace Sidner.
Keywords: Computer science, Cognitive psychology, and Robotics.read less