Director of Research (if dissertation) or Advisor (if thesis)
Roth, Dan
Doctoral Committee Chair(s)
Roth, Dan
Committee Member(s)
DeJong, Gerald F.
Hockenmaier, Julia C.
Mooney, Raymond
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Natural Language Processing
Machine Learning
Semantic Interpretation
Abstract
In this work we take a first step towards Learning from Natural Instructions (LNI), a framework for communicating human knowledge to computer systems using natural language. In this framework the process of learning is synonymous with language interpretation, the process in which natural language sentences are converted into a logical representation which can be understood by an automated agent.
While the motivation behind this framework is clear, the practical aspects involved in constructing it are non-trivial: communicating effectively with computer systems has been one of motivating forces behind artificial intelligence research since its inception. The rigid way in which computer systems naturally take instructions, via programming, and the flexible and ambiguous way in which humans naturally provide instructions, via natural language, rendered this task extremely difficult.
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