Abstract
There are several challenges for AI models of higher cognitive abilities like the profusion of knowledge, different forms of reasoning, the gap between neuro-inspired approaches and conceptual representations, the problem of inconsistent data, and the manifold of computational paradigms. The I-Cog architecture – proposed as a step towards a solution for these problems – consists of a reasoning device based on analogical reasoning, a rewriting mechanism operating on the knowledge base, and a neuro-symbolic interface for robust learning from noisy data. I-Cog is intended as a framework for human-level intelligence (HLI).
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Kühnberger, KU. et al. (2007). I-Cog: A Computational Framework for Integrated Cognition of Higher Cognitive Abilities. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_20
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