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Journal of Artificial General Intelligence

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A Measure of Real-Time Intelligence

Vaibhav Gavane
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  • School of Computing Science and Engineering VIT University Vellore - 632014, Tamil Nadu, India
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Published Online: 2014-04-25 | DOI: https://doi.org/10.2478/jagi-2013-0003


We propose a new measure of intelligence for general reinforcement learning agents, based on the notion that an agent’s environment can change at any step of execution of the agent. That is, an agent is considered to be interacting with its environment in real-time. In this sense, the resulting intelligence measure is more general than the universal intelligence measure (Legg and Hutter, 2007) and the anytime universal intelligence test (Hernández-Orallo and Dowe, 2010). A major advantage of the measure is that an agent’s computational complexity is factored into the measure in a natural manner. We show that there exist agents with intelligence arbitrarily close to the theoretical maximum, and that the intelligence of agents depends on their parallel processing capability. We thus believe that the measure can provide a better evaluation of agents and guidance for building practical agents with high intelligence.

Keywords: intelligence measure; reinforcement learning; real-time computation; algorithmic probability


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About the article

Received: 2012-06-30

Accepted: 2013-11-25

Published Online: 2014-04-25

Published in Print: 2013-03-01

Citation Information: Journal of Artificial General Intelligence, Volume 4, Issue 1, Pages 31–48, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/jagi-2013-0003.

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© Vaibhav Gavane. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. BY-NC-ND 3.0

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