SOAR: an architecture for general intelligence
Artificial Intelligence
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
A strategic metagame player for general chess-like games
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Journal of Logic, Language and Information
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Telling humans and computers apart automatically
Communications of the ACM - Information cities
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Environments for Multi-Agent Systems: First International Workshop, E4MAS, 2004, New York, NY, July 19, 2004, Revised Selected Papers (Lecture Notes in ... / Lecture Notes in Artificial Intelligence)
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
Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Machine Intelligence Quotient
Extending the Soar Cognitive Architecture
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Performance Evaluation and Benchmarking of Intelligent Systems
Performance Evaluation and Benchmarking of Intelligent Systems
A universal measure of intelligence for artificial agents
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability
General discounting versus average reward
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift
IEEE Computational Intelligence Magazine
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
Compression and intelligence: social environments and communication
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Measuring agent intelligence via hierarchies of environments
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Real-world limits to algorithmic intelligence
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
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
The arcade learning environment: an evaluation platform for general agents
Journal of Artificial Intelligence Research
How universal can an intelligence test be?
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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In this paper, we develop the idea of a universal anytime intelligence test. The meaning of the terms ''universal'' and ''anytime'' is manifold here: the test should be able to measure the intelligence of any biological or artificial system that exists at this time or in the future. It should also be able to evaluate both inept and brilliant systems (any intelligence level) as well as very slow to very fast systems (any time scale). Also, the test may be interrupted at any time, producing an approximation to the intelligence score, in such a way that the more time is left for the test, the better the assessment will be. In order to do this, our test proposal is based on previous works on the measurement of machine intelligence based on Kolmogorov complexity and universal distributions, which were developed in the late 1990s (C-tests and compression-enhanced Turing tests). It is also based on the more recent idea of measuring intelligence through dynamic/interactive tests held against a universal distribution of environments. We discuss some of these tests and highlight their limitations since we want to construct a test that is both general and practical. Consequently, we introduce many new ideas that develop early ''compression tests'' and the more recent definition of ''universal intelligence'' in order to design new ''universal intelligence tests'', where a feasible implementation has been a design requirement. One of these tests is the ''anytime intelligence test'', which adapts to the examinee's level of intelligence in order to obtain an intelligence score within a limited time.