<abstract xmlns="http://www.w3.org/1999/xhtml">Although animals such as spiders, fish, and birds have very different anatomies, the basic mechanisms that govern their perception, decision-making, learning, reproduction, and death have striking similarities. These mechanisms have apparently allowed the development of general intelligence in nature. This led us to the idea of approaching artificial general intelligence (AGI) by constructing a generic artificial animal (animat) with a configurable body and fixed mechanisms of perception, decision-making, learning, reproduction, and death. One instance of this generic animat could be an artificial spider, another an artificial fish, and a third an artificial bird. The goal of all decision-making in this model is to maintain homeostasis. Thus actions are selected that might promote survival and reproduction to varying degrees. All decision-making is based on knowledge that is stored in network structures. Each animat has two such network structures: a genotype and a phenotype. The genotype models the initial nervous system that is encoded in the genome (“the brain at birth”), while the phenotype represents the nervous system in its present form (“the brain at present”). Initially the phenotype and the genotype coincide, but then the phenotype keeps developing as a result of learning, while the genotype essentially remains unchanged. The model is extended to ecosystems populated by animats that develop continuously according to fixed mechanisms for sexual or asexual reproduction, and death. Several examples of simple ecosystems are given. We show that our generic animat model possesses general intelligence in a primitive form. In fact, it can learn simple forms of locomotion, navigation, foraging, language, and arithmetic.</abstract>

Department of Computer Science and Engineering

Combining Evolution and Learning in Computational Ecosystems

<abstract xmlns="http://www.w3.org/1999/xhtml">Natural selection has imbued biological agents with motivations moving them to act for survival and reproduction, as well as to learn so as to support both. Artificial agents also require motivations to act in a goal-directed manner and to learn appropriately into various memories. Here we present a biologically inspired motivation system, based on feelings (including emotions) integrated within the LIDA cognitive architecture at a fundamental level. This motivational system, operating within LIDA’s cognitive cycle, provides a repertoire of motivational capacities operating over a range of time scales of increasing complexity. These include alarms, appraisal mechanisms, appetence and aversion, and deliberation and planning.</abstract>

Computer Science Department and Institute for Intelligent Systems

Artificial Motivation for Cognitive Software Agents

<abstract xmlns="http://www.w3.org/1999/xhtml">After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways traditional reinforcement learning could be altered to remove this roadblock.</abstract>

The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI

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. 

Journal of Artificial General Intelligence

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

Combining Evolution and Learning in Computational Ecosystems

Artificial Motivation for Cognitive Software Agents

The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI

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