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oai:www.ideals.illinois.edu:2142/42235 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Roth, Dan Roth, Dan DeJong, Gerald F. Hockenmaier, Julia C. Mooney, Raymond Goldwasser, Dan 2013-02-03T19:28:46Z 2013-02-03T19:28:46Z 2012-12 2013-02-03T19:28:46Z 2012-12 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-12-04T19:04:05Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 GoldwasserDan.pdf: 1329164 bytes, checksum: 9c674e82762c7748d7114a2e570824bc (MD5) Made available in DSpace on 2013-02-03T19:28:46Z (GMT). No. of bitstreams: 7 Dan_Goldwasser.pdf: 1307116 bytes, checksum: 8c94c8bef8ef9fc689239975581544ef (MD5) GoldwasserDan.pdf: 1307107 bytes, checksum: 44c3ea7695576c270000c354eba437d5 (MD5) 1_GoldwasserDan.pdf: 1307161 bytes, checksum: b07ac6949d8c3d0d521d2465f8b59355 (MD5) 0_GoldwasserDan.pdf: 1307161 bytes, checksum: b07ac6949d8c3d0d521d2465f8b59355 (MD5) 1_0_GoldwasserDan.pdf: 1307161 bytes, checksum: b07ac6949d8c3d0d521d2465f8b59355 (MD5) 2_1_GoldwasserDan.pdf: 1307161 bytes, checksum: b07ac6949d8c3d0d521d2465f8b59355 (MD5) license.txt: 4064 bytes, checksum: 95e8de1017ae4235087da6dadaf161cd (MD5) http://hdl.handle.net/2142/42235 en Copyright 2012 Dan Goldwasser Artificial Intelligence Natural Language Processing Machine Learning Semantic Interpretation Learning from natural instructions text Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Computer Science -UIUC 10KS0112PHD