Rawan Ghnemat

Automata-based adaptive behavior for economic modeling using game theory

In this paper, we deal with some specific domains of applications to game theory. This is one of the major class of models in the new approaches of modelling in the economic domain. For that, we use genetic automata which allow to buid adaptive strategies for the players. We explain how the automata-based formalism proposed - matrix representation of automata with multiplicities - allows to define a semi-distance between the strategy behaviors. With that tools, we are able to generate an automatic processus to compute emergent systems of entities whose behaviors are represented by these genetic automata.

MODELING THE DYNAMICS OF COMPLEX INTERACTION SYSTEMS: FROM MORPHOGENESIS TO CONTROL

The aim of this paper is to contribute to the modeling and analysis of complex systems, taking into account the nature of complexity at different stages of the system life-cycle: from its genesis to its evolution. Therefore, some structural aspects of the complexity dynamics are highlighted, leading (i) to implement the morphogenesis of emergent complex network structures, and (ii) to control some synchronization phenomena within complex networks. Specific applications are proposed to illustrate these two aspects, in urban dynamics and in neural networks.

Self-Organization Simulation over Geographical Information Systems Based on Multi-Agent Platform

In this paper, we present a review concerning the coupling of Geographical Information Systems with agent-based simulation. With the development of new technologies and huge geographical databases, the geographers now deal with complex interactive networks which describe the new Geopolitics and world-wide Economy. The aim here, is to implement some self-organization processes that can emerge from these complex systems. We explain how we can today model such phenomena and how we can implement them in a practical way, using the concept of complex systems modelling and some efficient tools associated to this concept.

Hybrid Fuzzy Neural Search Retrieval System

Search engines are crucial for information gathering systems IGS. New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System MFIS, the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System ANFIS. The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.

Community Swarm Optimization

The development of distributed computations and complex systems modelling [11] leads to the creation of innovative algorithms based on interacting virtual entities, specifically for optimisation purposes within complex phenomena. Particule Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) are two of these algorithms. We propose in this paper a method called Community Swarm Optimisation (CSO). This method is based on more sophisticated entities which are defined by behavioral automata. This algorithm leads to the emergence of the solution by the co-evolution of their behavioral and spatial characteristics. This method is suitable for urban management, in order to improve the understanding of the individual behaviors over the emergent urban organizations.

Agent-based modeling using swarm intelligence in geographical information systems

In 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.