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.
Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.
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.
Chief Executive Editor
Pei Wang, Temple University, USA
Ben Goertzel, Novamente LLC, USA
Kai-Uwe Kühnberger, University of Osnabrück, Germany
Tsvi Achler, ITOP CORP / Optimizing Mind, USA
Joscha Bach, Intel Labs, USA
Farshad Badie, Aalborg University, Denmark
Tarek Besold, Alpha Health AI Lab, Spain
Haris Dindo, Yewno Inc., USA
Wlodzislaw Duch, Nicolaus Copernicus University, Poland
Stan Franklin, University of Memphis, USA
Patrick Hammer, Stockholm University, Sweden
Jose Hernandez-Orallo, Universitat Politecnica de Valencia, Spain
Pascal Hitzler, Wright State University, USA
Marcus Hutter, DeepMind, UK / Australian National University, Australia
Randal Koene, Carboncopies.org, USA
Christian Lebiere, Carnegie Mellon University, USA
Moshe Looks, McD Tech Labs, USA
Jim Marshall, Sarah Lawrence College, USA
Dagmar Monett, HWR Berlin / AGISI.org, Germany
Laurent Orseau, Google DeepMind, UK
Giovanni Pezzulo, Institute of Cognitive Sciences and Technologies, Italy
Florin Popescu, Fraunhofer Institute FIRST, Germany
Alexey Potapov, ITMO University, Russia
Brandon Rohrer, iRobot, USA
Paul Rosenbloom, University of Southern California, USA
Ute Schmid, Bamberg University, Germany
Jürgen Schmidhuber, Dalle Molle Institute for AI, Switzerland
Daniel Silver, Acadia University, Canada
Leslie Smith, University of Stirling, UK
Javier Snaider, Google, USA
Claes Strannegård, University of Gothenburg, Sweden
Kristinn Thorisson, Reykjavik University, Iceland
Mary-Anne Williams, The University of Technology, Sydney, Australia
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JAGI is an Open Access journal, with articles distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
For detailed author information, visit the submission website of JAGI.
Open Access Statement
The journal is an Open Access journal that allows a free unlimited access to all its contents without any restrictions upon publication to all users.