AI and Similarity

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http://doi.ieeecomputersociety.org/10.1109/MIS.2006.38

May/June 2006 (vol. 21 no. 3) pp. 39-49

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	Edwina L. Rissland, "AI and Similarity," IEEE Intelligent Systems, vol. 21, no. 3, pp. 39-49, May/June, 2006.

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	@article{ 10.1109/MIS.2006.38, author = {Edwina L. Rissland}, title = {AI and Similarity}, journal ={IEEE Intelligent Systems}, volume = {21}, number = {3}, issn = {1541-1672}, year = {2006}, pages = {39-49}, doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2006.38}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - MGZN JO - IEEE Intelligent Systems TI - AI and Similarity IS - 3 SN - 1541-1672 SP39 EP49 EPD - 39-49 A1 - Edwina L. Rissland, PY - 2006 KW - similarity KW - explanation KW - examples KW - concepts KW - concept change KW - case-based reasoning KW - hypotheticals KW - open-texture KW - AI and law VL - 21 JA - IEEE Intelligent Systems ER -

Edwina L. Rissland, University of Massachusetts

For AI to become truly robust, we must further our understanding of similarity-driven reasoning, analogy, learning, and explanation. Here are some suggested research directions.This article is part of a special issue on the Future of AI.

Index Terms:

similarity, explanation, examples, concepts, concept change, case-based reasoning, hypotheticals, open-texture, AI and law

Citation:

Edwina L. Rissland, "AI and Similarity," IEEE Intelligent Systems, vol. 21, no. 3, pp. 39-49, May/June 2006, doi:10.1109/MIS.2006.38

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