Philosophy-Guided Modelling and Implementation of Adaptation and Control
in Complex Systems
- URL: http://arxiv.org/abs/2009.00110v4
- Date: Sat, 25 Sep 2021 19:27:46 GMT
- Title: Philosophy-Guided Modelling and Implementation of Adaptation and Control
in Complex Systems
- Authors: Olivier Del Fabbro and Patrik Christen
- Abstract summary: We add to our already existing method the cybernetic concepts of control and especially adaptation.
We show how these new meta-theoretical concepts are described formally and how they are implemented in program code.
We conclude that philosophical abstract concepts help to better understand the process of creating computer models and their control and adaptation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Control was from its very beginning an important concept in cybernetics.
Later on, with the works of W. Ross Ashby, for example, biological concepts
such as adaptation were interpreted in the light of cybernetic systems theory.
Adaptation is the process by which a system is capable of regulating or
controlling itself in order to adapt to changes of its inner and outer
environment maintaining a homeostatic state. In earlier works we have developed
a system metamodel that on the one hand refers to cybernetic concepts such as
structure, operation, and system, and on the other to the philosophy of
individuation of Gilbert Simondon. The result is the so-called allagmatic
method that is capable of creating concrete models of systems such as
artificial neural networks and cellular automata starting from abstract
building blocks. In this paper, we add to our already existing method the
cybernetic concepts of control and especially adaptation. In regard to the
system metamodel, we rely again on philosophical theories, this time the
philosophy of organism of Alfred N. Whitehead. We show how these new
meta-theoretical concepts are described formally and how they are implemented
in program code. We also show what role they play in simple experiments. We
conclude that philosophical abstract concepts help to better understand the
process of creating computer models and their control and adaptation. In the
outlook we discuss how the allagmatic method needs to be extended in order to
cover the field of complex systems and Norbert Wiener's ideas on control.
Related papers
- Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces.
While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks.
This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
arXiv Detail & Related papers (2024-04-29T04:51:05Z) - Brain-Inspired Machine Intelligence: A Survey of
Neurobiologically-Plausible Credit Assignment [65.268245109828]
We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors.
The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
arXiv Detail & Related papers (2023-12-01T05:20:57Z) - Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks.
Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Intrinsic Physical Concepts Discovery with Object-Centric Predictive
Models [86.25460882547581]
We introduce the PHYsical Concepts Inference NEtwork (PHYCINE), a system that infers physical concepts in different abstract levels without supervision.
We show that object representations containing the discovered physical concepts variables could help achieve better performance in causal reasoning tasks.
arXiv Detail & Related papers (2023-03-03T11:52:21Z) - The dynamics of belief: continuously monitoring and visualising complex
systems [0.0]
Rise of AI in human contexts places new demands on automated systems to be transparent and explainable.
We develop a theoretical framework for thinking about digital systems in complex human contexts.
arXiv Detail & Related papers (2022-08-11T11:51:35Z) - The least-control principle for learning at equilibrium [65.2998274413952]
We present a new principle for learning equilibrium recurrent neural networks, deep equilibrium models, or meta-learning.
Our results shed light on how the brain might learn and offer new ways of approaching a broad class of machine learning problems.
arXiv Detail & Related papers (2022-07-04T11:27:08Z) - Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization [49.00409552570441]
We study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction.
We apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase.
arXiv Detail & Related papers (2022-06-03T12:24:49Z) - Mesarovician Abstract Learning Systems [0.0]
Current approaches to learning hold notions of problem domain and problem task as fundamental precepts.
Mesarovician abstract systems theory is used as a super-structure for learning.
arXiv Detail & Related papers (2021-11-29T18:17:32Z) - The Evolution of Concept-Acquisition based on Developmental Psychology [4.416484585765028]
A conceptual system with rich connotation is key to improving the performance of knowledge-based artificial intelligence systems.
Finding a new method to represent concepts and construct a conceptual system will greatly improve the performance of many intelligent systems.
Developmental psychology carefully observes the process of concept acquisition in humans at the behavioral level.
arXiv Detail & Related papers (2020-11-26T01:57:24Z) - Synergetic Learning Systems: Concept, Architecture, and Algorithms [4.623783824925363]
We describe an artificial intelligence system called the Synergetic Learning Systems''
The system achieves intelligent information processing and decision-making in a given environment through cooperative/competitive synergetic learning.
It is expected that under our design criteria, the proposed system will eventually achieve artificial general intelligence through long term coevolution.
arXiv Detail & Related papers (2020-05-31T06:23:03Z) - Philosophy-Guided Mathematical Formalism for Complex Systems Modelling [0.0]
We recently presented the so-called allagmatic method, which includes a system metamodel.
A mathematical formalism is presented to better describe and define the system metamodel of the allagmatic method.
arXiv Detail & Related papers (2020-05-03T21:20:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.