Center for Analysis & Design of Intelligent Agents /
School of Computer Science Reykjavik University 101 Reykjavik, Iceland & Icelandic Institute for Intelligent Machines 2.h. Uranus 101 Reykjavik, Iceland

Center for Analysis & Design of Intelligent Agents /
School of Computer Science Reykjavik University 101 Reykjavik, Iceland

Autonomous Systems Laboratory Universidad Politécnica de Madrid José Gutiérrez Abascal 2 28006 Madrid, Spain

Department of Computer and Information Sciences,
Temple University Philadelphia, PA 19122, USA

Editorial: Approaches and Assumptions of Self-Programming
in Achieving Artificial General Intelligence

<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">To solve a problem, an ordinary computer system executes an existing program. When
no such program is available, an AGI system may still be able to solve a concrete problem
instance. This paper introduces a new approach to do so in a reasoning system that
adapts to its environment and works with insuffcient knowledge and resources. The related
approaches are compared, and several conceptual issues are analyzed. It is concluded that
an AGI system can solve a problem with or without a problem-specific program, and
therefore can have human-like creativity and exibility.</abstract>

Solving a Problem With or Without a Program

<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">The ability to learn constructions may be important for the development of a self-organizing
architecture for artificial general intelligence. Constructions are structural relations between more
specific or more abstract conceptual representations. They can be derived from the processes
of alignment, collocations and distributed equivalences. An architecture that integrates in situ
grounded representations with cognitive productivity is ideally suited to learn constructions.
This paper described such an architecture, based on neuronal assembly structures and neuronal
’blackboards’ for grounded compositional representations. The paper outlines how constructions
could be learned in such an architecture and how the architecture could eventually develop into an
autonomous self-organizing architecture for artificial general intelligence.</abstract>

Technical Cognition, CTIT, University of Twente, P.O. Box 217, Enschede, 7500 AE, The Netherlands

IOP, Leiden University Leiden, 2333 AH, The Netherlands

Towards a constructivist methodology: learning constructions by
integrating in situ representations and productivity

<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">A logic is a mathematical model of knowledge used to study how we reason, how we describe
the world, and how we infer the conclusions that determine our behavior. The logic presented
here is natural. It has been experimentally observed, not designed. It represents knowledge as a
causal set, includes a new type of inference based on the minimization of an action functional,
and generates its own semantics, making it unnecessary to prescribe one. This logic is suitable
for high-level reasoning with computer code, including tasks such as self-programming, objectoriented
analysis, refactoring, systems integration, code reuse, and automated programming from
sensor-acquired data.
A strong theoretical foundation exists for the new logic. The inference derives laws of
conservation from the permutation symmetry of the causal set, and calculates the corresponding
conserved quantities. The association between symmetries and conservation laws is a fundamental
and well-known law of nature and a general principle in modern theoretical Physics. The conserved
quantities take the form of a nested hierarchy of invariant partitions of the given set. The logic
associates elements of the set and binds them together to form the levels of the hierarchy. It is
conjectured that the hierarchy corresponds to the invariant representations that the brain is known
to generate. The hierarchies also represent fully object-oriented, self-generated code, that can be
directly compiled and executed (when a compiler becomes available), or translated to a suitable
programming language.
The approach is constructivist because all entities are constructed bottom-up, with the
fundamental principles of nature being at the bottom, and their existence is proved by construction.
The new logic is mathematically introduced and later discussed in the context of transformations
of algorithms and computer programs. We discuss what a full self-programming
capability would really mean. We argue that self-programming and the fundamental question
about the origin of algorithms are inextricably linked. We discuss previously published, fully
automated applications to self-programming, and present a virtual machine that supports the logic,
an algorithm that allows for the virtual machine to be simulated on a digital computer, and a fully
explained neural network implementation of the algorithm.</abstract>

Reasoning with Computer Code: a new Mathematical Logic

<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">The AGINAO is a project to create a human-level artificial general intelligence system (HL AGI)
embodied in the Aldebaran Robotics' NAO humanoid robot. The dynamical and open-ended
cognitive engine of the robot is represented by an embedded and multi-threaded control program,
that is self-crafted rather than hand-crafted, and is executed on a simulated Universal Turing
Machine (UTM). The actual structure of the cognitive engine emerges as a result of placing the
robot in a natural preschool-like environment and running a core start-up system that executes
self-programming of the cognitive layer on top of the core layer. The data from the robot's sensory
devices supplies the training samples for the machine learning methods, while the commands sent
to actuators enable testing hypotheses and getting a feedback. The individual self-created
subroutines are supposed to reflect the patterns and concepts of the real world, while the overall
program structure reflects the spatial and temporal hierarchy of the world dependencies. This
paper focuses on the details of the self-programming approach, limiting the discussion of the
applied cognitive architecture to a necessary minimum.</abstract>

The AGINAO Self-Programming Engine

AHEAD OF PRINT

Volume 14 (2023): Issue 1 (March 2023)

Volume 13 (2022): Issue 1 (October 2022)

Volume 12 (2021): Issue 1 (January 2021)

Volume 11 (2020): Issue 2 (February 2020) Special Issue “On Defining Artificial Intelligence” — Commentaries and Author’s Response

Volume 11 (2020): Issue 1 (January 2020)

Volume 10 (2019): Issue 2 (January 2019)

Volume 10 (2019): Issue 1 (January 2019)

Volume 9 (2018): Issue 1 (March 2018)

Volume 8 (2017): Issue 1 (December 2017)

Volume 7 (2016): Issue 1 (December 2016)

Volume 6 (2015): Issue 1 (December 2015)

Volume 5 (2014): Issue 1 (December 2014)

Volume 4 (2013): Issue 3 (December 2013) Brain Emulation and Connectomics: a Convergence of Neuroscience and Artificial General Intelligence, Editors: Randal Koene and Diana Deca

Volume 4 (2013): Issue 2 (December 2013) Conceptual Commitments of AGI Systems, Editors:	Haris Dindo, James Marshall, and Giovanni Pezzulo

Volume 4 (2013): Issue 1 (November 2013)

Volume 3 (2012): Issue 3 (December 2012) Self-Programming and Constructivist Methodologies for AGI,
 Editors: Kristinn R. Thórisson, Eric Nivel and Ricardo Sanz

Volume 3 (2012): Issue 2 (November 2012)

Volume 3 (2012): Issue 1 (May 2012)

Volume 2 (2010): Issue 2 (December 2010) Cognitive Architectures, Model Comparison, and AGI, Editors: Christian Lebiere, Cleotilde Gonzalez and Walter Warwick

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

Volume 1 (2009): Issue 1 (December 2009)

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. 

Volume 3 (2012): Issue 3 (December 2012) - Self-Programming and Constructivist Methodologies for AGI, Editors: Kristinn R. Thórisson, Eric Nivel and Ricardo Sanz

Journal of Artificial General Intelligence

Volume 3 (2012): Issue 3 (December 2012)

Self-Programming and Constructivist Methodologies for AGI, Editors: Kristinn R. Thórisson, Eric Nivel and Ricardo Sanz

Editorial: Approaches and Assumptions of Self-Programming in Achieving Artificial General Intelligence

Solving a Problem With or Without a Program

Towards a constructivist methodology: learning constructions by integrating in situ representations and productivity

Reasoning with Computer Code: a new Mathematical Logic

The AGINAO Self-Programming Engine

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