Date of Award

Fall 12-15-2023

Level of Access Assigned by Author

Open-Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

Advisor

Roy Turner

Second Committee Member

Chaofan Chen

Third Committee Member

Phillip Dickens

Additional Committee Members

James Fastook

Bruce Segee

Abstract

Over the last three decades, a considerable amount of research has been dedicated to improving an artificial agent's ability to recognize and deal effectively with context. In this paper, I discuss a framework for a novel form of contextual reasoning. Unlike existing contextual reasoning frameworks, which allow an agent to apply its contextual knowledge after it is operating in an instance of a known context, the model I discuss allows an agent to reason about context proactively. With a proactive model, an agent forecasts the future contexts it will encounter, then takes steps to ensure its behaviors are appropriate for those contexts before entering into them.

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