Graph-based reasoning and reinforcement learning for improving Q/A performance in large knowledge-based systems

Abhishek Sharma*, Kenneth D Forbus

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Learning to plausibly reason with minimal user intervention could significantly improve knowledge acquisition. We describe how to integrate graph-based heuristic generalization, higher-order knowledge, and reinforcement learning to learn to produce plausible inferences with only small amounts of user training. Experiments on ResearchCyc KB contents show significant improvement in Q/A performance with high accuracy.

Original languageEnglish (US)
Title of host publicationCommonsense Knowledge - Papers from the AAAI Fall Symposium, Technical Report
Pages96-101
Number of pages6
StatePublished - Dec 1 2010
Event2010 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 11 2010Nov 13 2010

Publication series

NameAAAI Fall Symposium - Technical Report
VolumeFS-10-02

Other

Other2010 AAAI Fall Symposium
CountryUnited States
CityArlington, VA
Period11/11/1011/13/10

ASJC Scopus subject areas

  • Engineering(all)

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