Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Journal of Logic, Language and Information
Universal Intelligence: A Definition of Machine Intelligence
Minds and Machines
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
Frequency adjusted multi-agent Q-learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Measuring universal intelligence: Towards an anytime intelligence test
Artificial Intelligence
On more realistic environment distributions for defining, evaluating and developing intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Comparing humans and AI agents
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
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Evaluating agent intelligence is a fundamental issue for the understanding, construction and improvement of autonomous agents. New intelligence tests have been recently developed based on an assessment of task complexity using algorithmic information theory. Some early experimental results have shown that these intelligence tests may be able to distinguish between agents of the same kind, but they do not place very different agents, e.g., humans and machines, on a correct scale. It has been suggested that a possible explanation is that these tests do not measure social intelligence. One formal approach to incorporate social environments in an intelligence test is the recent notion of Darwin-Wallace distribution. Inspired by this distribution we present several new test settings considering competition and cooperation, where we evaluate the "social intelligence" of several reinforcement learning algorithms. The results show that evaluating social intelligence.