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

極小

Embracing Inference as Action: A Step Towards Human-Level Reasoning,o more additional features: ., which are a sort of frame in which the reasoning of an individual agent can be simulated, and ., which allow our reasoner to carry out operations beyond the limits of many current systems. We explain how these features are achieved and implemented in the MATR reasoning system, and discuss their consequences.

Bricklayer

Conference proceedings 2016pers, and 10 poster papers presented were carefully reviewed and selected from 67 submissions. AGI research differs from the ordinary AI research by stressing on the versatility and wholeness of intelligence, and by carrying out the engineering practice according to an outline of a system comparable to the human mind inSelf a certain sense..

exercise

3D Rover Localization in Airborne Ladar Data The VRL setup allows us to remove the incentive to wirehead by placing a constraint on the agent’s actions. The constraint is defined in terms of the agent’s belief distributions, and does not require an explicit specification of which actions constitute wireheading.

煩人

遺留之物

根除

Friction and Sliding Phenomena,veral meta-rules that govern the development of its network-based memory structure. The decision making mechanism operates under time constraints and combines symbolic reasoning, aimed at compressing information, with sub-symbolic reasoning, aimed at planning.

急急忙忙

Self-Help-Group

Imitation Learning as Cause-Effect Reasoning,hysical robot, which successfully generalizes skills involving bimanual manipulation of composite objects in 3D. These results suggest that cause-effect reasoning is an effective unifying principle for cognitive-level imitation learning.

Blazon

Real-Time GA-Based Probabilistic Programming in Application to Robot Control,ries are simply executed in parallel with plan execution by a robot meaning that they continuously optimize dynamically varying objective functions tracking their optima. Experiments with the NAO robot showed that replanning can be naturally done within this approach without developing special plan recovery methods.

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