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

Zadeh’s Fuzzy Sets are extended with the Dempster-Shafer Theory of Evidence into a new mathematical structure called Evidence Sets, which can capture more efficiently all recognized forms of uncertainty in a formalism that explicitly models the subjective context dependencies of linguistic categories. A belief-based theory of Approximate Reasoning is proposed for these structures. Evidence sets are then used in the development of a relational database architecture useful for the data mining of information stored in several networked databases. This useful data mining application establishes an Artificial Intelligence model of Cognitive Categorization with a hybrid architecture that possesses both connectionist and symbolic attributes.

... in distributed information systems is proposed. This system is both a model of dynamic cognitive categorization processes and powerful real application useful for knowledge management. It utilizes an extension of fuzzy sets named evidence sets as the mathematical mechanisms to implement the categorization processes. It is a development of some aspects of Pask’s Conversation Theory. It is also an instance of the notion of linguistic-based selected self-organization here described, and as such it instantiates an open-ended semiosis between distributed information systems and the communities of users they interact with. This means that the knowledge stored in distributed information resources adapts to the evolving semantic expectations of their users as these select the information they desire in conversation with the information resources. This way, this recommendation system establishes a mechanism for user-driven knowledge self-organization.

Agent-based modeling has proven to be particularly effective for fine-grain social modeling. However, so far, the modeling of social agents has not involved dynamics based on interpretive interaction. Meaning or interpretation plays a significant role in human interactions. These micro effects might create interesting effects on aggregate dynamics. The goal of the Interpretive Agent (IA) project is to develop a framework in which agent-based modeling can incorporate interpretive mechanisms. We believe that introducing interpretive mechanisms in agents will improve the depth and quality of the simulation models [Sallach, 2003; Sallach & Mellarkod 2004]. The innovation of interpretive behavior in agent models involves at least the following three mechanisms: prototype inference, orientation accounting and situational definition [Sallach 2003]. These mechanisms follow closely the mode by which human interpretation works. Introducing these mechanisms is not straight-forward, as the computational complexity generated by these mechanisms needs to be carefully aligned and integrated. We are working on each of these mechanisms separately and putting them together in the broad framework. The prototype structures are viewed as clusters formed from objects/events/actions of agents' responses. Thus, the work summarized in this paper captures the computational aspects of prototype structure and inference.