8,313 research outputs found
Optimisation in ‘Self-modelling’ Complex Adaptive Systems
When a dynamical system with multiple point attractors is released from an arbitrary initial condition it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimises these constraints by this method is unlikely, or may take many attempts. Here we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimise total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ‘recalling’ low energy states that have been previously visited but ‘predicting’ their location by generalising over local attractor states that have already been visited. This ‘self-modelling’ framework, i.e. a system that augments its behaviour with an associative memory of its own attractors, helps us better-understand the conditions under which a simple locally-mediated mechanism of self-organisation can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph colouring and distributed task allocation problems
String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors
* Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02This paper shows some ideas about how to incorporate a string learning stage in self-organizing
algorithms. T. Kohonen and P. Somervuo have shown that self-organizing maps (SOM) are not restricted to
numerical data. This paper proposes a symbolic measure that is used to implement a string self-organizing map
based on SOM algorithm. Such measure between two strings is a new string. Computation over strings is
performed using a priority relationship among symbols; in this case, symbolic measure is able to generate new
symbols. A complementary operation is defined in order to apply such measure to DNA strands. Finally, an
algorithm is proposed in order to be able to implement a string self-organizing map
Cooperative Game Theory within Multi-Agent Systems for Systems Scheduling
Research concerning organization and coordination within multi-agent systems
continues to draw from a variety of architectures and methodologies. The work
presented in this paper combines techniques from game theory and multi-agent
systems to produce self-organizing, polymorphic, lightweight, embedded agents
for systems scheduling within a large-scale real-time systems environment.
Results show how this approach is used to experimentally produce optimum
real-time scheduling through the emergent behavior of thousands of agents.
These results are obtained using a SWARM simulation of systems scheduling
within a High Energy Physics experiment consisting of 2500 digital signal
processors.Comment: Fourth International Conference on Hybrid Intelligent Systems (HIS),
Kitakyushu, Japan, December, 200
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Parallel Graph Partitioning for Complex Networks
Processing large complex networks like social networks or web graphs has
recently attracted considerable interest. In order to do this in parallel, we
need to partition them into pieces of about equal size. Unfortunately, previous
parallel graph partitioners originally developed for more regular mesh-like
networks do not work well for these networks. This paper addresses this problem
by parallelizing and adapting the label propagation technique originally
developed for graph clustering. By introducing size constraints, label
propagation becomes applicable for both the coarsening and the refinement phase
of multilevel graph partitioning. We obtain very high quality by applying a
highly parallel evolutionary algorithm to the coarsened graph. The resulting
system is both more scalable and achieves higher quality than state-of-the-art
systems like ParMetis or PT-Scotch. For large complex networks the performance
differences are very big. For example, our algorithm can partition a web graph
with 3.3 billion edges in less than sixteen seconds using 512 cores of a high
performance cluster while producing a high quality partition -- none of the
competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach
arXiv:1402.328
Trail Systems as fault tolerant wires and their use in bio-processors
This work constitute one of the thematics being developed in the Bioputing group, part of the Epigenomic project, Genopole, Evry.Motivated by the idea that one day, probably far in the future, the computers and robots will be architectureless, made of collections of numerous 'intelligent' subsystems or nanomachines able to self-organize each other into computational morphologies with perhaps more computational power than classical electronic-based computers, many studies are burgeoning in different fields (chemistry, biology, condensed matter, quantum physics, ...). Several systems inspired from Nature have indeed been proposed yet for designing unconventional computer architectures using processing modes of various nature and at different scales. The heterogeneous set of natural or artificially designed systems called trail systems, commonly associated to self-driven particles (agents1 ) with tropistic activity (through a communication based on traces let in the environment), is a soft matter with self-organizing properties sufficiently robust and fine for designing biocomputing structures. In this context, individual trails systems could be viewed as single wires and logical gates in a self-organized bio-processor, in the same manner axons are connecting the neural nodes in a neuro-processor. Their efficiency as wires depends on their specific properties which are often related to their scale. The robustness of their self-organization at the microscopic scale level occurring in a noisy environment, can be studied by a model based on effective computing systems (i.e. Turing machines) programmed to behave first as deterministic and perfect trail systems, then as stochastic-working trailing agents subject to randomness
Filtered Networks of Evolutionary Processors
* Supported by INTAS 00-626 and TIC 2003-09319-c03-03.This paper presents some connectionist models that are widely used to solve NP-problems. Most well
known numeric models are Neural Networks that are able to approximate any function or classify any pattern set
provided numeric information is injected into the net. Neural Nets usually have a supervised or unsupervised
learning stage in order to perform desired response. Concerning symbolic information new research area has
been developed, inspired by George Paun, called Membrane Systems. A step forward, in a similar Neural
Network architecture, was done to obtain Networks of Evolutionary Processors (NEP). A NEP is a set of
processors connected by a graph, each processor only deals with symbolic information using rules. In short,
objects in processors can evolve and pass through processors until a stable configuration is reach. This paper
just shows some ideas about these two models
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