Neural Computation
Text compression as a test for artificial intelligence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
On the Length of Programs for Computing Finite Binary Sequences
Journal of the ACM (JACM)
A Theory of Program Size Formally Identical to Information Theory
Journal of the ACM (JACM)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Journal of Logic, Language and Information
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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
Measuring universal intelligence: Towards an anytime intelligence test
Artificial Intelligence
A Monte-Carlo AIXI approximation
Journal of Artificial Intelligence Research
Self-modification and mortality in artificial agents
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Delusion, survival, and intelligent agents
AGI'11 Proceedings of the 4th international conference on Artificial general 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
Evaluating a reinforcement learning algorithm with a general intelligence test
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Complexity-based induction systems: Comparisons and convergence theorems
IEEE Transactions on Information Theory
How universal can an intelligence test be?
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the 'animal kingdom' are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term 'potential ability' usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different for the 'machine kingdom'. While machines can be characterised by a set of cognitive abilities, and measuring them is already a big challenge, known as 'universal psychometrics', a more informative, and yet more challenging, goal would be to also determine the potential cognitive abilities of a machine. In this paper we investigate the notion of potential cognitive ability for machines, focussing especially on universality and intelligence. We consider several machine characterisations (non-interactive and interactive) and give definitions for each case, considering permanent and temporal potentials. From these definitions, we analyse the relation between some potential abilities, we bring out the dependency on the environment distribution and we suggest some ideas about how potential abilities can be measured. Finally, we also analyse the potential of environments at different levels and briefly discuss whether machines should be designed to be intelligent or potentially intelligent.