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Abstract Understanding the process of categorization is a primary research goal in artificial intelligence. The conceptual space framework provides a flexible approach to modeling context-sensitive categorization via a geometrical representation designed for modeling and managing concepts. In this paper we show how algorithms developed in computational geometry, and the Region Connection Calculus can be used to model important aspects of categorization in conceptual spaces. In particular, we demonstrate the feasibility of using existing geometric algorithms to build and manage categories in conceptual spaces, and we show how the Region Connection Calculus can be used to reason about categories and other conceptual regions. 1 Introduction Categorization is a fundamental cognitive activity. The ability to classify and identify objects with a high degree of exception tolerance is a hallmark of intelligence, and an essential skill for learning and communication. Understanding the processes involved in constructing categories is a primary research goal in artificial intelligence. The conceptual space framework as developed by GĻardenfors [2000] provides a flexible approach to modeling context-sensitive categorization. Conceptual spaces are based on a simple, yet powerful, geometrical representation designed for modeling and managing concepts. In this paper we show how algorithms developed in computational geometry, and the Region Connection Calculus (RCC) [Cohn et al., 1997], a well known region-based spatial reasoning framework, can be used to model important aspects of categorization in conceptual spaces. In particular, we demonstrate the feasibility of using existing geometric algorithms to build and manage categories in conceptual spaces, and we show how the RCC can be used to reason about categories and other conceptual regions. ĪThis paper appeared in the Proceedings of the Fourteenth International Joint Conference of Artificial Intelligence, Morgan Kaufmann, 385 - 392, 2001. 2 Conceptual Spaces Conceptual spaces provide a framework for modeling the formation and the evolution of concepts. They can be used to explain psychological phenomena, and to design intelligent agents [Chella et al., 1998; GĻardenfors, 2000]. For the purposes of this paper conceptual spaces provide the necessary infrastructure for modeling the process of categorization. Conceptual spaces are geometrical structures based on quality dimensions. Quality dimensions correspond to the ways in which stimuli are judged to be similar or different.
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