Using Knowledge Containers to Model a Framework for Learning Adaptation Knowledge

Using Knowledge Containers to Model a Framework for Learning Adaptation Knowledge. Wilke, W., Vollrath, I., & Bergmann, R. In Wettschereck, D. & Aha, D. W., editors, European Conference on Machine Learning. MLNet Workshop Notes - Case-Based Learning: Beyond Classification of Feature Vectors, pages 68–75, Naval Research Laboratory, Washington, D. C., USA, 1997. Navy Center for Applied Research in Artificial Intelligence.

Paper abstract bibtex

In this paper, we present a framework for learning adaptation knowledge which we call knowledge light approaches for case-based reasoning (CBR) systems. Knowledge light means that these approaches use already acquired knowledge inside the CBR system. Therefore, we describe the sources of knowledge inside a CBR system along the different knowledge containers. After that we present our framework in terms of these knowledge containers. Further, we apply our framework in a case study to one knowledge light approach for learning adaptation knowledge. After that we point on some issues which should be addressed during the design or the use of such algorithms for learning adaptation knowledge. From our point of view many of these issues should be the topic of further research. Finally, we close with a short discussion and an outlook to further work.

@inproceedings{wilke_using_1997,
	title        = {Using {Knowledge} {Containers} to {Model} a {Framework} for {Learning} {Adaptation} {Knowledge}},
	author       = {Wilke, Wolfgang and Vollrath, Ivo and Bergmann, Ralph},
	year         = 1997,
	booktitle    = {European {Conference} on {Machine} {Learning}. {MLNet} {Workshop} {Notes} - {Case}-{Based} {Learning}: {Beyond} {Classification} of {Feature} {Vectors}},
	publisher    = {Navy Center for Applied Research in Artificial Intelligence},
	address      = {Naval Research Laboratory, Washington, D. C., USA},
	pages        = {68--75},
	url          = {http://www.wi2.uni-trier.de/publications/1997_WilkeVollrathBergmannECML.pdf},
	abstract     = {In this paper, we present a framework for learning adaptation knowledge which we call knowledge light approaches for case-based reasoning (CBR) systems. Knowledge light means that these approaches use already acquired knowledge inside the CBR system. Therefore, we describe the sources of knowledge inside a CBR system along the different knowledge containers. After that we present our framework in terms of these knowledge containers. Further, we apply our framework in a case study to one knowledge light approach for learning adaptation knowledge. After that we point on some issues which should be addressed during the design or the use of such algorithms for learning adaptation knowledge. From our point of view many of these issues should be the topic of further research. Finally, we close with a short discussion and an outlook to further work.},
	editor       = {Wettschereck, Dietrich and Aha, David W.},
	keywords     = {Adaptation, CBR, Container, Knowledge}
}

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