Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Journal of Logic, Language and Information
Telling humans and computers apart automatically
Communications of the ACM - Information cities
PAC model-free reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
A universal measure of intelligence for artificial agents
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Measuring universal intelligence: Towards an anytime intelligence test
Artificial Intelligence
A Monte-Carlo AIXI approximation
Journal of Artificial Intelligence Research
On more realistic environment distributions for defining, evaluating and developing intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
On measuring social intelligence: experiments on competition and cooperation
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
On Potential Cognitive Abilities in the Machine Kingdom
Minds and Machines
How universal can an intelligence test be?
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
| Hi-index | 0.00 |
Comparing humans and machines is one important source of information about both machine and human strengths and limitations. Most of these comparisons and competitions are performed in rather specific tasks such as calculus, speech recognition, translation, games, etc. The information conveyed by these experiments is limited, since it portrays that machines are much better than humans at some domains and worse at others. In fact, CAPTCHAs exploit this fact. However, there have only been a few proposals of general intelligence tests in the last two decades, and, to our knowledge, just a couple of implementations and evaluations. In this paper, we implement one of the most recent test proposals, devise an interface for humans and use it to compare the intelligence of humans and Q-learning, a popular reinforcement learning algorithm. The results are highly informative in many ways, raising many questions on the use of a (universal) distribution of environments, on the role of measuring knowledge acquisition, and other issues, such as speed, duration of the test, scalability, etc.