In this paper, we present a novel extension of CBR that allows cases to be more proactive at problem solving, by enriching case representations and facilitating richer interconnectedness between cases. We empirically study the improvements resulting from a holographic realization on experimental datasets. In addition to making CBR more cognitively appealing, the idea has the potential to lend itself as an elegant general CBR formalism of which diverse realizations of CBR can be viewed as instances.
CITATION STYLE
Ganesan, D., & Chakraborti, S. (2020). Holographic Case-Based Reasoning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12311 LNAI, pp. 144–159). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58342-2_10
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