Representation and reasoning for deeper natural language understanding in a physics tutoring system

Maxim Makatchev; Kurt VanLehn; Pamela W. Jordan; Umarani Pappuswamy

Representation and reasoning for deeper natural language understanding in a physics tutoring system

Maxim Makatchev, Kurt VanLehn, Pamela W. Jordan, Umarani Pappuswamy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Students' natural language (NL) explanations in the domain of qualitative mechanics lie in-between unrestricted NL and the constrained NL of "proper" domain statements. Analyzing such input and providing appropriate tutorial feedback requires extracting information relevant to the physics domain and diagnosing this information for possible errors and gaps in reasoning. In this paper we will describe two approaches to solving the diagnosis problem: weighted abductive reasoning and assumption-based truth maintenance system (ATMS). We also outline the features of knowledge representation (KR) designed to capture relevant semantics and to facilitate computational feasibility.

Original language	English (US)
Title of host publication	FLAIRS 2006 - Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference
Pages	682-687
Number of pages	6
Volume	2006
State	Published - 2006
Externally published	Yes
Event	FLAIRS 2006 - 19th International Florida Artificial Intelligence Research Society Conference - Melbourne Beach, FL, United States Duration: May 11 2006 → May 13 2006

Other

Other	FLAIRS 2006 - 19th International Florida Artificial Intelligence Research Society Conference
Country/Territory	United States
City	Melbourne Beach, FL
Period	5/11/06 → 5/13/06

ASJC Scopus subject areas

General Engineering

Cite this

Representation and reasoning for deeper natural language understanding in a physics tutoring system. / Makatchev, Maxim; VanLehn, Kurt; Jordan, Pamela W. et al.
FLAIRS 2006 - Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference. Vol. 2006 2006. p. 682-687.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Makatchev, M, VanLehn, K, Jordan, PW & Pappuswamy, U 2006, Representation and reasoning for deeper natural language understanding in a physics tutoring system. in FLAIRS 2006 - Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference. vol. 2006, pp. 682-687, FLAIRS 2006 - 19th International Florida Artificial Intelligence Research Society Conference, Melbourne Beach, FL, United States, 5/11/06.

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AB - Students' natural language (NL) explanations in the domain of qualitative mechanics lie in-between unrestricted NL and the constrained NL of "proper" domain statements. Analyzing such input and providing appropriate tutorial feedback requires extracting information relevant to the physics domain and diagnosing this information for possible errors and gaps in reasoning. In this paper we will describe two approaches to solving the diagnosis problem: weighted abductive reasoning and assumption-based truth maintenance system (ATMS). We also outline the features of knowledge representation (KR) designed to capture relevant semantics and to facilitate computational feasibility.

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Y2 - 11 May 2006 through 13 May 2006

ER -

Representation and reasoning for deeper natural language understanding in a physics tutoring system

Abstract

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ASJC Scopus subject areas

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