4,340 research outputs found
The Evolution of complexity in self-maintaining cellular information processing networks
We examine the role of self-maintenance (collective autocatalysis) in the evolution of computational biochemical networks. In primitive proto-cells (lacking separate genetic machinery) self-maintenance is a necessary condition for the direct reproduction and inheritance of what we here term Cellular Information Processing Networks (CIPNs). Indeed, partially reproduced or defective CIPNs may generally lead to malfunctioning or premature death of affected cells. We explore the interaction of this self-maintenance property with the evolution and adaptation of CIPNs capable of distinct information processing abilities. We present an evolutionary simulation platform capable of evolving artificial CIPNs from a bottom-up perspective. This system is an agent-based multi-level selectional Artificial Chemistry (AC) which employs a term rewriting system called the Molecular Classifier System (MCS). The latter is derived from the Holland broadcast language formalism. Using this system, we successfully evolve an artificial CIPN to improve performance on a simple pre-specified information processing task whilst subject to the constraint of continuous self-maintenance. We also describe the evolution of self-maintaining, crosstalking and multitasking, CIPNs exhibiting a higher level of topological and functional complexity. This proof of concept aims at contributing to the understanding of the open-ended evolutionary growth of complexity in artificial systems
Bottom-up construction of ontologies
Presents a particular way of building ontologies that proceeds in a bottom-up fashion. Concepts are defined in a way that mirrors the way their instances are composed out of smaller objects. The smaller objects themselves may also be modeled as being composed. Bottom-up ontologies are flexible through the use of implicit and, hence, parsimonious part-whole and subconcept-superconcept relations. The bottom-up method complements current practice, where, as a rule, ontologies are built top-down. The design method is illustrated by an example involving ontologies of pure substances at several levels of detail. It is not claimed that bottom-up construction is a generally valid recipe; indeed, such recipes are deemed uninformative or impossible. Rather, the approach is intended to enrich the ontology developer's toolki
The Self-Organization of Meaning and the Reflexive Communication of Information
Following a suggestion of Warren Weaver, we extend the Shannon model of
communication piecemeal into a complex systems model in which communication is
differentiated both vertically and horizontally. This model enables us to
bridge the divide between Niklas Luhmann's theory of the self-organization of
meaning in communications and empirical research using information theory.
First, we distinguish between communication relations and correlations among
patterns of relations. The correlations span a vector space in which relations
are positioned and can be provided with meaning. Second, positions provide
reflexive perspectives. Whereas the different meanings are integrated locally,
each instantiation opens global perspectives--"horizons of meaning"--along
eigenvectors of the communication matrix. These next-order codifications of
meaning can be expected to generate redundancies when interacting in
instantiations. Increases in redundancy indicate new options and can be
measured as local reduction of prevailing uncertainty (in bits). The systemic
generation of new options can be considered as a hallmark of the
knowledge-based economy.Comment: accepted for publication in Social Science Information, March 21,
201
Knowledge graph analysis of particles in Japanese
The theory of knowledge graphs is a structuralistic theory of language. Its ontology consists of eight types of binary relationships and four types of so-called frames. The relationships connect so-called tokens, that represent semantic units. In this way a graph structure arises. Japanese particles are investigated against the background of knowledge graph ontology. It is shown that the structure of Japanese closely resembles the structure of the knowledge graph representation of language
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
The Kinetic Basis of Self-Organized Pattern Formation
In his seminal paper on morphogenesis (1952), Alan Turing demonstrated that
different spatio-temporal patterns can arise due to instability of the
homogeneous state in reaction-diffusion systems, but at least two species are
necessary to produce even the simplest stationary patterns. This paper is aimed
to propose a novel model of the analog (continuous state) kinetic automaton and
to show that stationary and dynamic patterns can arise in one-component
networks of kinetic automata. Possible applicability of kinetic networks to
modeling of real-world phenomena is also discussed.Comment: 8 pages, submitted to the 14th International Conference on the
Synthesis and Simulation of Living Systems (Alife 14) on 23.03.2014, accepted
09.05.201
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