9 – CONCEPTUAL CLUSTERING AND CATEGORIZATION: Bridging the Gap between Induction and Causal Models

Categorization processes are central to many human capabilities; e.g., language, reasoning, problem solving. The concept of categorization is also at the base of many kinds of phenomenon which AI researchers have attempted to model; e.g., induction, analogy, and the use of causal models. Most approaches to induction can be characterized on a single dimension such as model driven, “top-down” to data driven, “bottom-up.” At the one end a large amount of preconstructed information (knowledge rich) is used while on the other end the featural similarity is analyzed of a given set of objects or events in the absence of other knowledge structures. These two kinds of approaches, represented recently by explanation-based learning (EBL) and similarity-based learning (SBL), conflict in terms of the proper approach to categorization and construction of causal theories. One view central to the present approach is that featural information is instrumental in formation of knowledge structures. Knowledge structures can be more general than objects and can possess more complex information than features (e.g., abstract concepts, actions, relations). Such knowledge structures are hypothesized to be both created and further manipulated by the SBL mechanism that learned them in the first place. The present approach is related to the discovery of category structure and the use of feature intercorrelations and their interaction with generalization, inheritance, retrieval, and memory organization.