Different goals in multiscale simulations and how to reach them

In this paper we sum up our works on multiscale programs, mainly simulations. We first start with describing what multiscaling is about, how it helps perceiving signal from a background noise in a ?ow of data for example, for a direct perception by a user or for a further use by another program. We then give three examples of multiscale techniques we used in the past, maintaining a summary, using an environmental marker introducing an history in the data and finally using a knowledge on the behavior of the different scales to really handle them at the same time

Control on System Diffusion Using Genetic Automata

International audienceComplex systems \citep{Moigne1999} are models which describe dynamic organizations or systems where the internal interaction network between their components does not allow the control of individual components with efficience. In a lot of cases, the individual control leads to break the whole structure itself. In other cases, some complex systems exhibit resilience properties which lead them to recover their initial state after small perturbations or individual controls. Controlling complex systems need to not trying to manage the individual controls but to manage the global control, guiding the whole system without directly act on individuals themselves. The study presented in this paper explains how to control a high level system property, the diffusive behavior, by spectral analysis over genetic automata. First experiments are presented using MuPad implementation

Shift operators and complex systems

International audienceIn this paper, we deal with some multiagent systems modelling, based on population of automata.We focus our attention with automatic computation of emerging systems. A multiscale representation is proposed here and consists in representing the internal states of an agent behaviour by a automaton with multiplicities, on the one hand and an adaptive global system behaviour by a genetic algorithm over a population of automata, on the other hand. This genetic process can lead to generate many new automata which behaviour can be eventually similar. The role played by shift operators is to identify these similar behaviours. Two applications are presented. The first one concerns adaptive strategies in game theory. The second one concerns an automatic emerging computation of self-organised multiagent systems based on the efficience of operation expressivity of automata with multiplicities

URBAN DYNAMICS MODELLING USING ANT NEST BUILDING

International audienceUrban dynamics deal with spatial organizations where a great complexity of interactions appears. Social and economic aspects interact and environmental objectives are nowadays a major purpose for sustainable urban development. We propose some generic modelling processes able to face with this complexity, in order to simulate the evolution of the city centers. These organizational centers need a multi-criteria description for their evolution, including feed-back phenomena of them over their environment and components. We propose a swarm intelligence algorithm, using social-insect collective behavior. We combine a decentralized approach, based on emergent clustering mixed with spatial constraints or attractions, as an extension of the ant nest building algorithm with multi-center. Typically, this model is currently used by ourself, to model and analyze cultural equipment dynamics in urban area

ON THE USE OF GENERALIZED DERANGEMENTS FOR SCHELLING'S MODEL OF SEGREGATION

International audienceThis paper proposes a definition of Schelling's model of segregation using generalized derangements. Many of urban or territorial modellings are based on decentralized approaches where rule-based systems have to be integrated inside a whole interaction system to describe complex phenomena. The goal of these decentralized modellings is to deal with emergent computing able to detect dynamically emergent organizations in an unsupervized way, thanks to complex systems theory. The convergence of these modern computings is generally hard to study because of the use of asynchronised processes dealing with a number of autonomous entities which are acting and interacting, in non linear way, during the whole simulation. Our approach is to define a non sequential-dependant algorithm, thanks to generalized derangements, and so to use this efficient tool to study some properties on the evolutive process

Modeling Spatial Organization with Swarm Intelligence Processes

International audienceUrban Dynamics modeling needs to implement spatial organization emergence in order to describe the development of services evolution and their usage within spatial centers. In this paper, we propose an extension of the nest building algorithm with multi-center, multi-criteria and adaptive processes. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. Typically, this model is suitable to analyse and simulate urban dynamics like the evolution of cultural equipment in urban area

A multi-step differential transform method and application to non-chaotic or chaotic systems

International audienceThe differential transform method (DTM) is an analytical and numerical method for solving a wide variety of differential equations and usually gets the solution in a series form. In this paper, we propose a reliable new algorithm of DTM, namely multi-step DTM, which will increase the interval of convergence for the series solution. The multi-step DTM is treated as an algorithm in a sequence of intervals for finding accurate approximate solutions for systems of differential equations. This new algorithm is applied to Lotka-Volterra, Chen and Lorenz systems. Then, a comparative study between the new algorithm, multi- step DTM, classical DTM and the classical Runge-Kutta method is presented. The results demonstrate reliability and efficiency of the algorithm developed

Consistent Updating of Geographical DataBase as Emergent Property over Influence System

International audienceGeographic Information Systems (GIS) will be expected in the future to evolve as to integrate social simulations or ecological processes inside complex systems of active and interactive entities. In a more practical aspect, GIS has now to evolve to manage updating. We will explain how the updating processes can be described as an evolution processes for GIS and make them transform from complicated systems to complex systems. This evolution processus is decomposed through an influence table which corresponds to elementary canonical operations. In addition to these updating elementary operations, we verify, during the processus, the maintenancy of the whole Geographical Database (GDB) using a constraint-based system. Our goal is to make the updating satisfying the global consistent property which emerges from the computation over the whole evolutive system. In this paper, we will give the whole processus description which leads to obtain this emergent property of consistence from a dynamic propagation processus which allows to obtain the global consistence from a local satisfaction property

Agent-Based Modeling Using Swarm Intelligence in Geographical Information Systems

International audienceIn this paper swarm intelligence algorithms are presented to deal with dynamical and spatial Organization emergence. The goal is to model and simulate the development of spatial center and their dynamic interactions with the environment and the individuals; the swarm algorithms used are inspired from natural termite nest building and Ant culturing algorithm. Combination of decentralized approaches based on emergent clustering mixed with spatial multi criteria constraints or attractions developed , extension of termite nest building algorithms has been proposed to have multi center adaptive process. The modeling has been made using agent based modeling techniques and the simulation developed using Repast (REcursive Porous Agent Simulation Toolkit) and OpenMap as geographical information system (GIS) software, some simulations result are provided

Swarm intelligence for urban dynamics modeling

International audienceIn this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area