Modeling the evolution of knowledge and reasoning in learning systems

Abhishek Sharma*, Kenneth D Forbus

*Corresponding author for this work

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

Abstract

How do reasoning systems that learn evolve over time? Characterizing the evolution of these systems is important for understanding their limitations and gaining insights into the interplay between learning and reasoning. We describe an inverse ablation model for studying how learning and reasoning interact: Create a small knowledge base by ablation, and incrementally re-add facts, collecting snapshots of reasoning performance of the system to measure properties of interest. Experiments with this model suggest that different concepts show different rates of growth, and that the density of facts is an important parameter for modulating the rate of learning.

Original languageEnglish (US)
Title of host publicationCommonsense Knowledge - Papers from the AAAI Fall Symposium, Technical Report
Pages102-107
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)

Fingerprint Dive into the research topics of 'Modeling the evolution of knowledge and reasoning in learning systems'. Together they form a unique fingerprint.

Cite this