Representation and Reasoning for Deeper Natural Language Understanding in a Physics Tutoring System

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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA520367

Entities

People

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

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Cognition
  • Cognitive Science
  • Computer Languages
  • Data Sets
  • Language
  • Natural Language Processing
  • Natural Language Understanding
  • Natural Languages
  • Pattern Recognition
  • Psychology
  • Reasoning
  • Students

Readers

  • Artificial Intelligence