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Explanation-based generalization: A unifying view

Tom M. Mitchell, Richard M. Keller, Smadar T. Kedar-Cabelli
1986 Machine Learning  
The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research.  ...  This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization.  ...  Acknowledgments The perspective on Explanation-Based Generalization reported here has arisen from discussions over a period of time with a number of people in the Machine Learning Group at Rutgers and  ... 
doi:10.1007/bf00116250 fatcat:nngighwmczgkvhvpkkdxevyls4

Knowledge Level and Inductive Uses of Chunking (EBL) [chapter]

Paul S. Rosenbloom, Jans Aasman
1992 Soar: A Cognitive Architecture in Perspective  
When explanation-based learning (EBL) is used for knowledge level learning (KLL), training examples 1  ...  Explanation-Based Learning Using the chunking/EBL mechanism to perform explanation-based learning -that is, the standard form of symbol level learning -supports the third, and final leg of the integrated  ...  , explanation-based learning, and its use in induction; and conclude.  ... 
doi:10.1007/978-94-011-2426-3_8 fatcat:lpm2ke3lbfcwreiby5ve3chq7m

Towards a Theoretical Base for Educational Multimedia Design

Tom Boyle
2002 Journal of Interactive Media in Education  
This section explores how the internal structure, the morphology, of contexts might best be delineated for capture in a systematic knowledge base.  ...  The aim of this paper is to contribute to the construction of a systematic theoretical base for educational multimedia design. The paper delineates different layers of explanation.  ...  Towards a theoretical base for educational multimedia design Boyle Journal of Interactive Media in Education, 2002 (2) Page 15  ... 
doi:10.5334/2002-2 fatcat:4kkx5ckvzbgqjhuidd6mdtaypm

Explanation-based learning: An alternative view

Gerald Dejong, Raymond Mooney
1986 Machine Learning  
In the last issue of this journal Mitchell, Keller, and Kedar-Cabelli presented a unifying framework for the explanation-based approach to machine learning.  ...  While it works well for a number of systems, the framework does not adequately capture certain aspects of the systems under development by the explanation-based learning group at Illinois.  ...  Acknowledgements The authors benefited greatly from discussions with other members of the explanation-based learning group at Illinois: Paul O'Rorke, Alberto Segre, Jude Shavlik, Shankar Rajamoney, Scott  ... 
doi:10.1007/bf00114116 fatcat:zkqjpyqdxndgdi4tlobfk3ssey

Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem

William W. Cohen
1992 Machine Learning  
This paper proposes an extension of explanation-based learning, called abductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques  ...  One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is the multiple inconsistent explanation problem.  ...  A solution to this problem would be an important step toward integration of explanation-based and similarity-based approaches to learning.  ... 
doi:10.1007/bf00992863 fatcat:5wquk237n5d4jmosh22ajojksq

Towards a hypothetical learning progression of scientific explanation

Jian-Xin Yao, Yu-Ying Guo, Knut Neumann
2016 Asia-Pacific Science Education  
Then we design a hypothetical learning progression of scientific explanation, based on the Phenomena-Theory-Data-Reasoning framework.  ...  Derived from that framework, a learning progression of scientific explanations has been proposed for the entry point of K-12 science education.  ...  Acknowledgement This research was conducted as part of project: The Instructional Design Based on Core Concepts and Practices' Progression (grant No. 13YJA880022), supported by the Ministry of Education  ... 
doi:10.1186/s41029-016-0011-7 fatcat:tgodfuc3nbcm7apou2erryhlxa

A layered analysis of self-explanation and structured reflection to support clinical reasoning in medical students

Martine Chamberland, Silvia Mamede, Linda Bergeron, Lara Varpio
2020 Perspectives on Medical Education  
The layered analysis of self-explanation and structured reflection offers essential insights into the underpinnings of these interventions.  ...  Building on the similarities between self-explanation and structured reflection, while also harnessing their differences, we identify why and how these interventions can be combined in a single implementation  ...  Underpinning philosophy of self-explanation in medicine There are two theories underlying self-explanation in medicine: generative learning theory and the theory of expertise in medicine.  ... 
doi:10.1007/s40037-020-00603-2 pmid:32734591 fatcat:ct72hpjoerflzgmwbmswxsoffy

Learning to cope with an open world

Boi Faltings
1996 Artificial intelligence for engineering design, analysis and manufacturing  
However, there are few theories, and no computer systems, that would allow us to design structures with a similar degree of automation.  ...  Thus, learning is essential for creating powerful intelligent design systems. I distinguish two forms of learning in design:  ...  The technique of explanation-based learning can then be applied to learn new design knowledge in an automatic and reliable manner. I have shown two examples of such a process.  ... 
doi:10.1017/s0890060400001402 fatcat:tlmze2z3inatfpljk4qz42bska

Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases [chapter]

Ashwin Ram
1993 Case-Based Learning  
We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations.  ...  The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c)  ...  The term "explanation-based refinement" is due to DeJong and Mooney (1986) .  ... 
doi:10.1007/978-1-4615-3228-6_2 fatcat:zjwxdld2qbd4hjbr5hcy3mwpi4

Problem structure and evidential reasoning

1988 International Journal of Approximate Reasoning  
This paper documents the use of a Bayesian method in a real-time problem that is similar to medical diagnosis in that there is a need to form decisions and take some action without complete knowledge of  ...  After experimenting with a number of nonprobabilistic methods for dealing with uncertainty, many researchers reaffirm a preference for probability methods, although this remains controversial.  ...  T-BIL integrates deductive learning, based on a technique called Explanation-Based Generalization (EBG) from the field of machine learning, with inductive learning methods from Bayesian decision theory  ... 
doi:10.1016/0888-613x(88)90164-8 fatcat:ajaos63ruzezvofuo6qdpbcckm

Learning strategies for explanation patterns: Basic game patterns with application to chess [chapter]

Yaakov Kerner
1995 Lecture Notes in Computer Science  
In this paper we describe game-independent strategies, capable of learning explanation patterns (XPs) for evaluation of any basic game pattern.  ...  A basic game pattern is defined as a minimal configuration of a small number of pieces and squares which describes only one salient game feature. Each basic pattern can be evaluated by a suitable XP.  ...  Therefore, we adopt Kass's theory as a basis for our learning theory. We have developed an extended theory of learning strategies for evaluation of game positions.  ... 
doi:10.1007/3-540-60598-3_45 fatcat:xvz4bj4bivbjzeniccun5b67li

How to Teach Evolution

Jan-Eric Mattsson, Ann Mutvei
2015 Procedia - Social and Behavioral Sciences  
Out of identified general didactic problems when teaching evolutionary theory, structured learning situations for pre-service teacher students were created together with performance assessment.  ...  One conclusion is the strength in open questions, promoting the students' creation of reasonable explanations within a theoretical framework.  ...  Further, we are greatly indebted to one anonymous reviewer for valuable suggestions of improvement of the manuscript.  ... 
doi:10.1016/j.sbspro.2014.12.658 fatcat:orx54w7bjbcl7nbyz7dl4bdhni

Comparative Approaches to Teaching Family Theory

Silvia Bartolic, Katherine Lyon, Laura Sierra, James White
2016 Family Science Review  
First, we discuss the current situation of teaching family theories based on formats of extant texts. Then we detail our proposed comparative approach.  ...  Finally, we detail two examples of how to use this comparative approach in teaching family theories. Background Our knowledge is always structured.  ...  Below, we provide an example of how students could learn about two different theories, social exchange and opportunity structure, in a comparative, applied manner.  ... 
doi:10.26536/fsr.2016.21.01.05 fatcat:utiln5cfmrc6tphxcz2g55dpse

News and notes

1987 Machine Learning  
Lebowitz addressed this issue with a learning method that integrates similarity-based learning and explanation-based learning.  ...  structures to accommodate a new explanation. • creativity -the modification and transfer of an explanation to a new, unanticipated situation.  ... 
doi:10.1007/bf00058755 fatcat:c5ixjwwgb5fzrifunchwl2wbf4

On the Basic Course of Inquiry-based Mathematics Learning

Yan-Wu DU
2017 DEStech Transactions on Social Science Education and Human Science  
This paper aims at the self-constructing course of mathematics knowledge in inquiry-based learning from problems representation, conjecture, evidence collection, forming explanation and survey, which has  ...  important theoretical and practical significance to the further understanding of the nature of the inquiry-based mathematics learning.  ...  The new cognitive structure indicates the forming of new knowledge in the current Inquiry-based learning, but it also represents the already known cognitive structure as a footstone of next circulation  ... 
doi:10.12783/dtssehs/icss2016/9128 fatcat:sxjpeutuq5awbp3kno6xgj7kwa
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