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By Bas Steunebrink, Pei Wang, Ben Goertzel

This ebook constitutes the refereed lawsuits of the ninth overseas convention on synthetic common Intelligence, AGI 2016, held in long island urban, long island, united states, in July 2016 as a part of HLAI 2016, the Joint Multi-Conference on Human-Level synthetic Intelligence 2016.

The 24 complete papers, 2 brief papers, and 10 poster papers offered have been rigorously reviewed and chosen from sixty seven submissions. AGI examine differs from the standard AI examine by means of stressing at the versatility and wholeness of intelligence, and through conducting the engineering perform in response to an summary of a approach such as the human brain inSelf a definite sense.

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Additional resources for Artificial General Intelligence: 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings

Sample text

The universally intelligent agent AIXI constitutes a formal mathematical theory of artificial general intelligence [3]. AIXI models its environment using a universal mixture ξ over the class of all lower semi-computable semimeasures, and thus is able to learn any computable environment. Semimeasures are defective probability measures which may sum to less than 1. Originally devised for Solomonoff induction, they are necessary for universal artificial intelligence because the halting problem prevents the existence of a (lower semi-)computable universal measure for the class of (computable) measures [5].

To prove this claim we first define, for each environment semimeasure μ, a corresponding environment μ that has a death-state. Definition 4 (Equivalent death-state environment μ ). 3 • The death-reward rd = 0. • The μ -probability of all percepts except the death-percept is equal to the μ-probability: μ (et | æ

Steunebrink et al. ): AGI 2016, LNAI 9782, pp. 23–32, 2016. 1007/978-3-319-41649-6 3 24 J. Martin et al. The universally intelligent agent AIXI constitutes a formal mathematical theory of artificial general intelligence [3]. AIXI models its environment using a universal mixture ξ over the class of all lower semi-computable semimeasures, and thus is able to learn any computable environment. Semimeasures are defective probability measures which may sum to less than 1. Originally devised for Solomonoff induction, they are necessary for universal artificial intelligence because the halting problem prevents the existence of a (lower semi-)computable universal measure for the class of (computable) measures [5].

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