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.
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.
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.
Chief Executive Editor Pei Wang, Temple University, USA
Executive Editors Ben Goertzel, Novamente LLC, USA Kai-Uwe Kühnberger, University of Osnabrück, Germany
Editors Tsvi Achler, ITOP CORP / Optimizing Mind, USA Joscha Bach, AI Foundation, USA Tarek Besold, Alpha Health AI Lab, Spain Haris Dindo, Yewno Inc., USA Wlodzislaw Duch, Nicolaus Copernicus University, Poland Stan Franklin, University of Memphis, USA Jose Hernandez-Orallo, Universitat Politecnica de Valencia, Spain Pascal Hitzler, Wright State University, USA Marcus Hutter, 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, Facebook, 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