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Gaining Insight through Case-Based Explanation |
Nugent, Conor; Doyle, Donal; Cunningham, Padraig
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TCD-CS-2004-49 Because CBR is an interpretable process, it is a reasoning mechanism
that supports explanation. This can be done explicitly by the system designers
incorporating explanation patterns in cases. This can be termed
knowledge-intensive explanation in CBR. However, of more interest here
is case-based explanation that works by allowing users to consider the relation
between different cases. The recommendation of a decision support
system can be explained by presenting similar cases that motivate the
recommendation. Users can derive insight from similar cases that have
different outcomes. The differences in outcome are due to the differences
in the un-matching features (provided the effect is not due to noisy data).
This is a more knowledge-light approach to case-based explanation. This
is appropriate for weak-theory domains where the details of the causal interactions
in the domain are not well understood; experts would however
be able to express the direction of causal interactions. In this paper we
present such a knowledge-light framework for Case-Based Explanation.
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Keyword(s):
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Computer Science |
Publication Date:
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2004 |
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Type:
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Report |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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Trinity College Dublin |
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Citation(s):
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Nugent, Conor; Doyle, Donal; Cunningham, Padraig. 'Gaining Insight through Case-Based Explanation'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2004-49, 2004, pp21 |
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Publisher(s):
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Trinity College Dublin, Department of Computer Science |
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File Format(s):
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application/pdf |
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First Indexed:
2014-05-13 05:31:17 Last Updated:
2015-04-10 05:13:56 |
