Knowledge Spaces
A Greedy Knowledge Acquisition Method for the Rapid Prototyping of Bayesian Belief Networks
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Proceedings of the Third international conference on Formal Concept Analysis
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Learning the DAG of bayesian belief networks by asking (conditional) (in-)dependence questions
Proceedings of the fifth international conference on Knowledge capture
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
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The main goal of this paper is the presentation of a new GReedy knowledge Acquisition Procedure (GRAP) for rapid prototyping of knowledge structures (KS) or spaces. The classical knowledge acquisition method for this [2] is even for domain experts cognitive demanding and computational complex. GRAP interactively generates an online knowledge acquisition schedule so that experts only have to provide simple nonredundant judgements about the (learning / cognitive) precedence in pairs of (learning / cognitive) objects. From these data GRAP generates a Hasse diagram of the surmise relation from which the knowledge structures and optimal user-adaptive learning paths can be derived. In a case-study we developed with three expert software engineers a knowledge structure and optimal learning paths for 23 software design patterns within a few hours.