Explanation-based generalization: A unifying view

By Tom Michael Mitchell, Richard M. Keller, Smadar T. Kedar-Cabelli. 1986

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Marc Hanheide
2. Hendrik Zender
3. Moritz Göbelbecker
4. Patric Jensfelt
5. Graham S. Horn
6. Andrzej Pronobis
7. Geert-Jan M. Kruijff
8. Alper Aydemir

Citations in the corpus

• Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, methodology): “Thus, the abductive reasoning process used to infer explanations were also used to generalise properties – a parallel seen in machine learning [133].”
• A Preliminary Survey of Explanation Facilities of AI-Based Design Support Approaches and Tools (2018, unknown): “Explanationbased generalization [9] is an approach for generalization of training examples in machine learning using explanation-based learning [5], an approach for supervised learning by explaining the results of the training (e.”
• Robot task planning and explanation in open and uncertain worlds (2017, background): “There is also a relationship to work in explanation-based learning [30].”