Titlebook: Artificial General Intelligence; 9th International Co Bas Steunebrink,Pei Wang,Ben Goertzel Conference proceedings 2016 Springer Internatio

hegemony

Integrating Axiomatic and Analogical Reasoning,e reasoning mechanism combines axiomatic reasoning for drawing conclusions inside the domains, with analogical reasoning for transferring knowledge from one domain to another. Thus the reasoning processes may use analogies to jump back and forth between domains.

CLASH

0302-9743 ntelligence, AGI 2016, held in New York City, NY, USA, in July 2016 as part of HLAI 2016, the Joint Multi-Conference on Human-Level Artificial Intelligence 2016...The 24 full papers, 2 short papers, and 10 poster papers presented were carefully reviewed and selected from 67 submissions. AGI research

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子女

conquer

Tempor

Different Conceptions of Learning: Function Approximation vs. Self-Organization, learning community, while the latter is exemplified by the AGI system NARS. This paper describes the learning mechanism of NARS, and contrasts it with canonical machine learning algorithms. It is concluded that inferential learning is arguably more fundamental for AGI systems.

FLIRT

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3D Rover Localization in Airborne Ladar Datanot work well for generally intelligent agents, as RL agents are incentivised to shortcut the reward sensor for maximum reward – the so-called .. In this paper we suggest an alternative to RL called value reinforcement learning (VRL). In VRL, agents use the reward signal to learn a utility function.

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Kai O. Arras,Nicola Tomatis,Roland Siegwartlogy and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over . that need not sum to 1, rather than over proper probability measures. In this work we argue that the shortfall of a s

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M. R. Sirouspour,S. E. Salcudeanonceptual jump size measures in AIT to stochastic problems, and physical measures that involve volume and energy. We introduce a graphical model of computational complexity that we believe to be appropriate for intelligent machines. We show several asymptotic relations between energy, logical depth