Artificial General Intelligence: 6th International by Ahmed M. H. Abdel-Fattah, Ulf Krumnack, Kai-Uwe Kühnberger

By Ahmed M. H. Abdel-Fattah, Ulf Krumnack, Kai-Uwe Kühnberger (auth.), Kai-Uwe Kühnberger, Sebastian Rudolph, Pei Wang (eds.)

This publication constitutes the refereed lawsuits of the sixth overseas convention on synthetic common Intelligence, AGI 2013, held in Beijing, China, in July/August 2013. The 23 papers (17 complete papers, three technical communications, and three targeted consultation papers) have been conscientiously reviewed and chosen from numerous submissions. the amount collects the present learn endeavors dedicated to boost formalisms, algorithms, and versions, in addition to platforms which are unique at normal intelligence. just like the predecessor AGI meetings, researchers proposed diversified methodologies and strategies with a purpose to bridge the space among varieties of really expert intelligence and common intelligence.

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Additional info for Artificial General Intelligence: 6th International Conference, AGI 2013, Beijing, China, July 31 – August 3, 2013 Proceedings

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1992–1997 (2010) 19. : An argument for an analogical perspective on rationality & decision-making. , Verbrugge, R. ) Proc. of the Workshop on Reasoning About Other Minds (RAOM 2011). CEUR Workshop Proceedings, vol. 751, pp. 20–31. org (July 2011) 20. : Towards a Domain-Independent Computational Framework for Theory Blending. AAAI Technical Report of the AAAI Fall 2011 Symposium on Advances in Cognitive Systems, pp. 210–217 (2011) 21. : Theory Blending as a Framework for Creativity in Systems for General Intelligence.

For visual pattern recognition, it appears to be a feature, since the hierarchical structure of such algorithms roughly mimics the architecture of visual cortex. For automated learning of computer programs carrying out symbolic tasks, on the other hand, CSDLN architectures are awkward at best and probably generally inappropriate. For cases like language learning or abstract conceptual inference, the jury is out. The question addressed here is: how to introduce an appropriate feedback between feature selection and learning in the case of machine learning algorithms with general scope and without explicit hierarchical structure.

Metaphors and Heuristic–Driven Theory Projection (HDTP). Theoretical Computer Science 354, 98–117 (2006) 13. : Syntactic principles of HeuristicDriven Theory Projection. Journal of Cognitive Systems Research 10(3), 251–269 (2009) 14. : A computational account of conceptual blending in basic mathematics. Journal of Cognitive Systems Research 12(3), 249–265 (2011) 15. : A note on inductive generalization. Machine Intelligence 5, 153–163 (1970) 16. : Restricted higher-order antiunification for analogy making.

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