Abstract
In this paper we review varied work done in the AI/Cognition Group at the chair for theoretical computer science and foundations of artificial intelligence aimed at a better understanding of the representational issues involved in the simulation of human cognition.
We consider representation to be a central issue in the construction of artificial systems to simulate human behavior, even in connectionist or “neural” approaches in which autonomous learning of representations is of primary interest. Those systems must be provided with some a priori structure in order for them to develop useful representations in a reasonable amount of time.
In the context of a system to “give directions in an urban setting” as paradigmatic application and long term goal, we discuss basic research in the areas of relational models for context-dependent knowledge representation and object description, on the one hand, and connectionist and evolutionary models of learning and adoption, on the other.
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Brauer, W. et al. (1990). Approaches to the Representation of Knowledge. In: Schwärtzel, H., Mizin, I.A. (eds) Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-93464-3_3
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DOI: https://doi.org/10.1007/978-3-642-93464-3_3
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