Principles of artificial intelligence
Principles of artificial intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The nature of statistical learning theory
The nature of statistical learning theory
Ant algorithms for discrete optimization
Artificial Life
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Understanding Intelligence
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
ICML '06 Proceedings of the 23rd international conference on Machine learning
Neural Computation
Training Recurrent Networks by Evolino
Neural Computation
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Anticipatory Behavior in Adaptive Learning Systems
AI in the 21st century - with historical reflections
50 years of artificial intelligence
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When Kurt Gödel layed the foundations of theoretical computer science in 1931, he also introduced essential concepts of the theory of Artificial Intelligence (AI). Although much of subsequent AI research has focused on heuristics, which still play a major role in many practical AI applications, in the new millennium AI theory has finally become a full-fledged formal science, with important optimality results for embodied agents living in unknown environments, obtained through a combination of theory à la Gödel and probability theory. Here we look back at important milestones of AI history, mention essential recent results, and speculate about what we may expect from the next 25 years, emphasizing the significance of the ongoing dramatic hardware speedups, and discussing Gödel-inspired, self-referential, self-improving universal problem solvers.