Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
On semimeasures predicting Martin-Löf random sequences
Theoretical Computer Science
Universal Intelligence: A Definition of Machine Intelligence
Minds and Machines
An analysis of model-based Interval Estimation for Markov Decision Processes
Journal of Computer and System Sciences
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
Optimality issues of universal greedy agents with static priors
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Is there an elegant universal theory of prediction?
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Universal knowledge-seeking agents
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Asymptotic non-learnability of universal agents with computable horizon functions
Theoretical Computer Science
Optimistic agents are asymptotically optimal
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Memory issues of intelligent agents
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Learning agents with evolving hypothesis classes
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
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Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no agent can satisfy the strong version while in some cases, depending on discounting, there does exist a non-computable weak asymptotically optimal agent.