Towards collaborative and adversarial learning:: a case study in robotic soccer
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Artificial Life
Journal of Logic, Language and Information
A Computer Scientist's View of Life, the Universe, and Everything
Foundations of Computer Science: Potential - Theory - Cognition, to Wilfried Brauer on the occasion of his sixtieth birthday
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Universal Intelligence: A Definition of Machine Intelligence
Minds and Machines
A Collection of Definitions of Intelligence
Proceedings of the 2007 conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006
Measuring universal intelligence: Towards an anytime intelligence test
Artificial Intelligence
Comparing humans and AI agents
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
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One insightful view of the notion of intelligence is the ability to perform well in a diverse set of tasks, problems or environments. One of the key issues is therefore the choice of this set, which can be formalised as a 'distribution'. Formalising and properly defining this distribution is an important challenge to understand what intelligence is and to achieve artificial general intelligence (AGI). In this paper, we agree with previous criticisms that a universal distribution using a reference universal Turing machine (UTM) over tasks, environments, etc., is perhaps amuch too general distribution, since, e.g., the probability of other agents appearing on the scene or having some social interaction is almost 0 for many reference UTMs. Instead, we propose the notion of Darwin-Wallace distribution for environments, which is inspired by biological evolution, artificial life and evolutionary computation. However, although enlightening about where and how intelligence should excel, this distribution has so many options and is uncomputable in so many ways that we certainly need a more practical alternative. We propose the use of intelligence tests over multi-agent systems, in such a way that agents with a certified level of intelligence at a certain degree are used to construct the tests for the next degree. This constructive methodology can then be used as a more realistic intelligence test and also as a testbed for developing and evaluating AGI systems.