Journal article
The Explanatory Power of Symbolic Similarity in Case-Based Reasoning
The Artificial intelligence review, v 24(2), pp 145-161
Oct 2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
A desired capability of automatic problem solvers is that they can explain the results. Such explanations should justify that the solution proposed by the problem solver arises from the known domain knowledge. In this paper we discuss how explanations can be used in case-based reasoning (CBR) in order to justify the results in classification tasks and also for solving new problems. We particularly focus on explanations derived from building a symbolic description of the similar aspects among cases. Moreover, we show how symbolic descriptions of similarity can be exploited in the different processes of CBR, namely retrieve, reuse, revise, and retain.
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Details
- Title
- The Explanatory Power of Symbolic Similarity in Case-Based Reasoning
- Creators
- Enric Plaza - IIIA Artificial Intelligence Research Institute CSIC Spanish Council for Scientific Research Campus UAB, 08193 Bellaterra Catalonia SpainEva Armengol - IIIA Artificial Intelligence Research Institute CSIC Spanish Council for Scientific Research Campus UAB, 08193 Bellaterra Catalonia SpainSantiago Ontañón - IIIA Artificial Intelligence Research Institute CSIC Spanish Council for Scientific Research Campus UAB, 08193 Bellaterra Catalonia Spain
- Publication Details
- The Artificial intelligence review, v 24(2), pp 145-161
- Publisher
- Kluwer Academic Publishers; Dordrecht
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000233242800003
- Scopus ID
- 2-s2.0-27744446220
- Other Identifier
- 991014878191204721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence