Argumentation Based Constraint Acquisition

Efficient acquisition of constraint networks is a key factor for the applicability of constraint problem solving methods. Current techniques ease knowledge acquisition by generating examples which are classified by a domain expert. However, in addition to this classification, an expert can usually provide arguments why examples should be rejected or accepted. Generally speaking domain specialists have partial knowledge about the theory to be acquired which can be exploited for knowledge acquisition. Based on this observation we discuss the various types of arguments a knowledge engineer can formulate. For the processing of these types of arguments we developed a knowledge acquisition algorithm which gives the knowledge engineer the possibility to input arguments in addition to the classification of examples. The result of this approach is a significant reduction of the number of examples which must be classified.

• Type: Journal
• Year: 2009
• Where: ICDM
• Authors: Kostyantyn M. Shchekotykhin, Gerhard Friedrich
• Keywords: Data Mining, Efficient Acquisition, ICDM 2009, Knowledge Acquisition, Knowledge Acquisition Algorithm