Hybrid Soft Categorization in Conceptual Spaces
Ickjai Lee · 2005
Understanding the process of categorization is of great importance for building intelligent agents. Formulated categories help agents find information easier and understand the external world better. Instance-based categorization and prototype-based categorization have been two dominant approaches in the AI community. However, they share some drawbacks in common. First, they are crisp boundary-based hard categorizations (similar to classification). Second, they are not well-suited for dynamic category learning and formation. We propose a hybrid soft categorization in the conceptual level that overcomes these drawbacks. The hybrid soft categorization merges the two popular hard categorizations and provides a robust fuzzy boundary-based soft categorization.