By Bas Steunebrink, Pei Wang, Ben Goertzel
This publication constitutes the refereed court cases of the ninth foreign convention on synthetic common Intelligence, AGI 2016, held in manhattan urban, new york, united states, in July 2016 as a part of HLAI 2016, the Joint Multi-Conference on Human-Level man made Intelligence 2016.
The 24 complete papers, 2 brief papers, and 10 poster papers provided have been conscientiously reviewed and chosen from sixty seven submissions. AGI examine differs from the normal AI study by way of stressing at the versatility and wholeness of intelligence, and by way of accomplishing the engineering perform in line with an summary of a procedure corresponding to the human brain inSelf a undeniable sense.
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Extra info for Artificial General Intelligence: 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings
PU (x) PU (x0) + PU (x1) (3) eliminating irrelevant programs and ensuring that the probabilities sum to 1, from which point on PU (x0|x) = PU (x0)/PU (x) yields an accurate prediction. 1 2 A preﬁx-free code is a set of codes in which no code is a preﬁx of another. A computer ﬁle uses a preﬁx-free code, ending with an EOF symbol, thus, most reasonable programming languages are preﬁx-free. We used the regular expression notation in language theory. Physical Complexity and Limits 35 The error bound for this method is the best known for any such induction method.
Optimizing energy eﬃciency of computation would also be an obviously useful goal for a self-improving AI. J as applied to inductive inference, which we agree with, and will eventually implement in our Alpha Phase 2 machine; Alpha Phase 1 has already been partially implemented in our parallel incremental inductive inference system . Acknowledgements. Thanks to anonymous reviewers whose comments substantially improved the presentation. Thanks to Gregory Chaitin and Juergen Schmidhuber for inspiring the mathematical philosophy/digital physics angle in the paper.
Any semimeasure ν can be turned into a measure νnorm using Solomonoﬀ normalisation . Simply let νnorm ( ) := 1 and ∀x ∈ X ∗ , y ∈ X : νnorm (xy) := νnorm (x) ν(y | x) ν(xy) , hence = ν(xz) ν norm (y | x) z∈X ν(z | x) (1) z∈X General reinforcement learning. In the general RL framework, the agent interacts with an environment in cycles: at each time step t the agent selects an action at ∈ A, and receives a percept et ∈ E. Each percept et = (ot , rt ) is a tuple consisting of an observation ot ∈ O and a reward rt ∈ R.
Artificial General Intelligence: 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings by Bas Steunebrink, Pei Wang, Ben Goertzel