Incremental learning via exceptions for agents and humans: evaluating KR comprehensibility and usability

Debbie Richards*, Meredith Taylor

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Acquiring knowledge directly from the domain expert requires a knowledge representation and specification method that is comprehensible and feasible for the holder and creator of that knowledge. The technique, known as multiple classification ripple down rules (MCRDR), is novelly applied to the problem of building and maintaining a library of training scenarios for use by customs and immigration officer trainees in our agent-based virtual environment which may be indexed for retrieval based on the rules associated with them. Our evaluation study aims to demonstrate the utility of the MCRDR combined case and exception structure rule-based approach over standard rules alone and a non-case-based approach.

Original languageEnglish
Title of host publicationPRICAI 2010: Trends in Artificial Intelligence - 11th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsByoung-Tak Zhang, Mehmet A. Orgun
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Pages655-661
Number of pages7
Volume6230 LNAI
ISBN (Print)3642152457, 9783642152450
DOIs
Publication statusPublished - 2010
Event11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010 - Daegu, Korea, Republic of
Duration: 30 Aug 20102 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6230 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010
CountryKorea, Republic of
CityDaegu
Period30/08/102/09/10

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