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Title:Learning from natural instructions
Author(s):Goldwasser, Dan
Director of Research:Roth, Dan
Doctoral Committee Chair(s):Roth, Dan
Doctoral Committee Member(s):DeJong, Gerald F.; Hockenmaier, Julia C.; Mooney, Raymond
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(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.
Issue Date:2013-02-03
URI:http://hdl.handle.net/2142/42235
Rights Information:Copyright 2012 Dan Goldwasser
Date Available in IDEALS:2013-02-03
Date Deposited:2012-12


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