Knowledge engineering: the applied side of artificial intelligence
Proc. of a symposium on Computer culture: the scientific, intellectual, and social impact of the computer
Machine learning: applications in expert systems and information retrieval
Machine learning: applications in expert systems and information retrieval
A survey of knowledge acquisition techniques and tools
Knowledge Acquisition
Varieties of knowledge elicitation techniques
International Journal of Human-Computer Studies
Applications of machine learning and rule induction
Communications of the ACM
Some challenges and grand challenges for computational intelligence
Journal of the ACM (JACM)
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Argument based machine learning
Artificial Intelligence
CG'06 Proceedings of the 5th international conference on Computers and games
Elicitation of neurological knowledge with ABML
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Deriving concepts and strategies from chess tablebases
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Goal-Oriented conceptualization of procedural knowledge
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
ABML knowledge refinement loop: a case study
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Elicitation of neurological knowledge with argument-based machine learning
Artificial Intelligence in Medicine
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Knowledge elicitation is known to be a difficult task and thus a major bottleneck in building a knowledge base. Machine learning has long ago been proposed as a way to alleviate this problem. Machine learning usually helps the domain expert to uncover some of the more tacit concepts. However, the learned concepts are often hard to understand and hard to extend. A common view is that a combination of a domain expert and machine learning would yield the best results. Recently, argument based machine learning (ABML) has been introduced as a combination of argumentation and machine learning. Through argumentation, ABML enables the expert to articulate his knowledge easily and in a very natural way. ABML was shown to significantly improve the comprehensibility and accuracy of the learned concepts. This makes ABML a most natural tool for constructing a knowledge base. The present paper shows how this is accomplished through a case study of building a knowledge base of an expert system used in a chess tutoring application.