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 e-book constitutes the refereed complaints of the sixth overseas convention on synthetic normal Intelligence, AGI 2013, held in Beijing, China, in July/August 2013. The 23 papers (17 complete papers, three technical communications, and three unique consultation papers) have been rigorously reviewed and chosen from a variety of submissions. the amount collects the present study endeavors dedicated to advance formalisms, algorithms, and versions, in addition to platforms which are specified at common intelligence. just like the predecessor AGI meetings, researchers proposed diverse methodologies and strategies so that it will bridge the distance among types of really expert intelligence and basic intelligence.

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James Cooke Brown around 1955 and first widely announced in a 1960 Scientific American article [3]. Loglan is still under development but now is not used nearly as widely as Lojban. First separated from Loglan in 1987, Lojban is a constructed language that lives at the border between natural language and computing language. It is a “natural-like language” in that it is speakable and writeable by humans and may be used by humans to discuss the same range of topics as natural languages. Lojban has a precise, specified formal syntax that can be parsed in the same manner as a programming language, and it has a semantics, based on predicate logic, in which ambiguity is carefully controlled.

However, if the MOSES learning process is being launched internally via OpenCog’s goal system, there is no opportunity for a human to adjust the feature selection heuristics based on the amount of light in the environment. Instead, MOSES has got to figure out what features to pay attention to all by itself. LIFES is designed to allow MOSES (or other comparable learning algorithms) to do this. So far we have tested LIFES in genomics and other narrow-AI application areas, as a way of initially exploring and validating the technique.

LIFES exposes MOSES to a much greater number of genes, some of which MOSES finds useful. And LIFES enables MOSES to explore this larger space of genes without getting bollixed by the potential combinatorial explosion of possibilities. Table 1. Impact of LIFES on MOSES classification of Alzheimers Disease SNP data. 5. Precision and recall figures are average figures over 10 folds, using 10-fold cross-validation. The results shown here are drawn from a larger set of runs, and are selected according to two criteria: best training precision (the fair way to do it) and best test precision (just for comparison).

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