New Millennium AI and the Convergence of History

@inproceedings{Schmidhuber2007NewMA,
  title={New Millennium AI and the Convergence of History},
  author={J{\"u}rgen Schmidhuber},
  booktitle={Challenges for Computational Intelligence},
  year={2007}
}
  • J. Schmidhuber
  • Published in
    Challenges for Computational…
    19 June 2006
  • Computer Science
Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At the same time there has been rapid progress in practical methods for learning true sequence-processing programs, as opposed to traditional methods limited to stationary pattern association. Here we… 

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References

SHOWING 1-10 OF 249 REFERENCES

The New AI: General & Sound & Relevant for Physics

TLDR
The new millennium has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam's razor, problem solving, decision making, and reinforcement learning in environments of a very general type.

Universal Artificial Intelligence

  • Dr. Marcus Hutter
  • Computer Science
    Texts in Theoretical Computer Science An EATCS Series
  • 2005
TLDR
UAI is an increasingly well-studied foundational theory for artificial intelligence, based on ancient principles in the philosophy of science and modern developments in information and probability theory, that provides a theoretically optimal agent AIXI and principled ideas for constructing practical autonomous agents.

Co-evolving recurrent neurons learn deep memory POMDPs

TLDR
A new neuroevolution algorithm called Hierarchical Enforced SubPopulations that simultaneously evolves networks at two levels of granularity: full networks and network components or neurons is introduced.

Reinforcement Learning: An Introduction

TLDR
This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.

Solving Non-Markovian Control Tasks with Neuro-Evolution

TLDR
This article demonstrates a neuroevolution system, Enforced Sub-populations (ESP), that is used to evolve a controller for the standard double pole task and a much harder, non-Markovian version, and introduces an incremental method that evolves on a sequence of tasks, and utilizes a local search technique (Delta-Coding) to sustain diversity.

Artificial curiosity based on discovering novel algorithmic predictability through coevolution

  • J. Schmidhuber
  • Computer Science
    Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
  • 1999
TLDR
A "curious" embedded agent that differs from previous explorers in the sense that it can limit its predictions to fairly arbitrary, computable aspects of event sequences and thus can explicitly ignore almost arbitrary unpredictable, random aspects.

Sequential Behavior and Learning in Evolved Dynamical Neural Networks

TLDR
This article explores the use of a real-valued modular genetic algorithm to evolve continuous-time recurrent neural networks capable of sequential behavior and learning and utilizes concepts from dynamical systems theory to understand the operation of some of these evolved networks.

Efficient Non-linear Control Through Neuroevolution

TLDR
A novel neuroevolution method called CoSyNE that evolves networks at the level of weights is introduced that is found to be significantly more efficient and powerful than the other methods on these tasks, forming a promising foundation for solving challenging real-world control tasks.

Efficient reinforcement learning through symbiotic evolution

TLDR
A new reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing a task, is presented.

Adaptation in natural and artificial systems

TLDR
Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
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