Conceptual Spaces as a Framework for Knowledge Representation

The dominating models of information processes have been based on symbolic representations of information and knowledge. During the last decades, a variety of non-symbolic models have been proposed as superior. The prime examples of models within the non-symbolic approach are neural networks. However, to a large extent they lack a higher-level theory of representation. In this paper, conceptual spaces are suggested as an appropriate framework for non-symbolic models. Conceptual spaces consist of a number of “quality dimensions” that often are derived from perceptual mechanisms. It will be outlined how conceptual spaces can represent various kind of information and how they can be used to describe concept learning. The connections to prototype theory will also be presented. 1. The Problem of Modeling Representations Cognitive science has two overarching goals. One is explanatory : By studying the cognitive activities of humans and other animals, one formulates theories of different aspects of cognition. The theories are tested by experiments or by computer simulations. The other goal is constructive: By building artifacts like chess-playing programs, robots, animats, etc., one attempts to construct systems that can accomplish various cognitive tasks. For both kinds of goals, a key problem is how the representations used by the cognitive system are to be modeled in an appropriate way. In cognitive science, there are currently two dominating approaches to the problem of modeling representations. The symbolic approach starts from the assumption that cognitive systems should be modeled by Turing machines. On this view, cognition is seen as essentially involving symbol manipulation. The second approach is associationism, where associations between different kinds of information elements carry the main burden of representation. Connectionism is a special case of associationism, which models associations by artificial neuron networks. Both the symbolic and the associationistic approaches have their advantages and disadvantages. They are often presented as competing paradigms, but since they attack