<abstract xmlns="http://www.w3.org/1999/xhtml"> One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby’s homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn’t be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.</abstract>

Homeostatic Agent for General Environment

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 8 (2017): Issue 1 (December 2017)

Homeostatic Agent for General Environment

Cerca

Tutti i volumi ed i numeri in questa rivista