Activating CBR Systems through Autonomous
Information Gathering

Christina Carrick and Qiang Yang
Simon Fraser University
Burnaby, BC, Canada, V5A 1S6
(ccarrick) (qyang)@cs.sfu.ca

Irene Abi-Zeid and Luc Lamontagne
Defense Research Establishment Valcartier
Decision Support Technology
2459, boul. Pie XI, nord
Val Belair, Quebec, Canada, G3J 1X5
(irene.abi-zeid) (luc.lamontagne)@drev.dnd.ca



Abstract. Most traditional CBR systems are passive in nature, adopting 
an advisor role in which a user manually consults the system. In this
paper, we propose a system architecture and algorithm for transforming
a passive interactive CBR system into an active, autonomous CBR system. 
Our approach is based on the idea that cases in a CBR system can
be used to model hypotheses in a situation assessment application, where
case attributes can be considered as questions or information tasks to be
performed on multiple information sources. Under this model, we can use
the CBR system to continually generate tasks that are planned for and
executed based on information sources such as databases, the Internet
or the user herself. The advantage of the system is that the majority
of trivial or repeated questions to information sources can be done autonomously 
through information gathering techniques, and human users
are only asked a small number of necessary questions by the system.
We demonstrate the application of our approach to an equipment diagnosis 
domain. We show that the system integrates CBR retrieval with
hierarchical query planning, optimization and execution.
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