<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">Measuring progress in the field of Artificial General Intelligence (AGI) can be difficult without commonly accepted methods of evaluation. An AGI benchmark would allow evaluation and comparison of the many computational intelligence algorithms that have been developed. In this paper I propose that a benchmark for natural world interaction would possess seven key characteristics: fitness, breadth, specificity, low cost, simplicity, range, and task focus. I also outline two benchmark examples that meet most of these criteria. In the first, the direction task, a human coach directs a machine to perform a novel task in an unfamiliar environment. The direction task is extremely broad, but may be idealistic. In the second, the AGI battery, AGI candidates are evaluated based on their performance on a collection of more specific tasks. The AGI battery is designed to be appropriate to the capabilities of currently existing systems. Both the direction task and the AGI battery would require further definition before implementing. The paper concludes with a description of a task that might be included in the AGI battery: the search and retrieve task.</abstract>

Accelerating progress in Artificial General Intelligence: Choosing a benchmark for natural world interaction

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Journal of Artificial General Intelligence

 Journal of Artificial General Intelligence (JAGI) is a peer-reviewed open-access academic journal, owned by the Artificial General Intelligence Society (AGIS).  Artificial General Intelligence (AGI) is an emerging field aiming at the building of "thinking machines", that is, general-purpose systems with intelligence comparable to that of the human mind. While this was the original goal of Artificial Intelligence (AI), the mainstream of AI research has turned toward domain-dependent and problem-specific solutions; therefore it has become necessary to use a new name to indicate research that still pursues the "Grand AI Dream". Similar labels for this kind of research include "Strong AI", "Human-level AI", etc.  The problems involved in creating general-purpose intelligent systems are very different from those involved in creating special-purpose systems. Therefore, this journal is different from conventional AI journals in its stress on the long-term potential of research towards the ultimate goal of AGI, rather than immediate applications. Articles focused on details of AGI systems are welcome, if they clearly indicate the relation between the special topics considered and intelligence as a whole, by addressing the generality, extensibility, and scalability of the techniques proposed or discussed.  Since AGI research is still in its early stage, the journal strongly encourages novel approaches coming from various theoretical and technical traditions, including (but not limited to) symbolic, connectionist, statistical, evolutionary, robotic and information-theoretic, as well as integrative and hybrid approaches.  Rejection Rate  &gt;30%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 

Journal of Artificial General Intelligence

Volume 2 (2010): Issue 1 (June 2010)

Accelerating progress in Artificial General Intelligence: Choosing a benchmark for natural world interaction

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