Abstract—Biological evolution has generated a rich variety of successful solutions; from nature, optimized strategies can be inspired. One interesting example is the ant colonies, which are able to exhibit a collective intelligence, still that their dynamic is simple. The emergence of different patterns depends on the pheromone trail, leaved by the foragers. It serves as positive feedback mechanism for sharing information. In this paper, we use the dynamic of TASEP as a model of interaction at a low level of the collective environment in the ant’s traffic flow. This work consists of modifying the movement rules of particles “ants” belonging to the TASEP model, so that it adopts with the natural movement of ants. Therefore, as to respect the constraints of having no more than one particle per a given site, and in order to avoid collision within a bidirectional circulation, we suggested two strategies: decease strategy and waiting strategy. As a third work stage, this is devoted to the study of these two proposed strategies’ stability. As a final work stage, we applied the first strategy to the whole environment, in order to get to the emergence of traffic flow, which is a way of learning. Keywords—Ants system, emergence, exclusion process, pheromone. I. INTRODUCTION IFFERENT species of animals use very sophisticated tools to communicate and assure continuity for their survival. In a similar situation, the environment plays an important role. Typical examples exist within social insects, such as colony of ants and bees, capable to exhibit very complex spatio- temporal patterns, without centralized control. Indeed, the chemical substance or “the pheromone trail”, leaved by the ants in the environment, it attracts the colony’s ants to its traces space, and then, insure a best exploitation of their environment [5]-[6]. In this context, the cooperation between individuals in the same colony or between colonies is a fundamental basis [8]. Amongst social insects, ants are the best example; with their cooperation and the communication’s system “stigmergy”, they arrive at to the food source certainly and return back to the nest with a sophistic system called colonial visa. These chemical substances leaved by ant plays an important role in the formation of complex behaviour emerging at different levels in their life. Referring to Muller and Channon [13]-[12]-[9], a phenomena is emergent if the dynamic interaction between entities in any system is expressed in a distinct theory different from the theory of observed phenomena. Another example is the competitive environment, which is different from the social insect environment; the predators employ a best strategy to mark their colony and defend their territory. In this case, the cooperation is familial. Whatever the kind of social insects or prey and predator animals, the emergence of different behaviours in their environment, exhibit a high level of interaction and cooperation proving their survival. In the last years, the asymmetric simple exclusion process (ASEP) became a very powerful tool of research in many disciplines of science such as, physics [2][3][4], chemistry [1][4] and biology, …etc. In computer science, few works were done using these processes as a way of dynamic modelling at micro level of complex system. We list as reference the work of Chowdhury and Nishinari [9], who developed particle-hopping models, formulated in terms of a stochastic cellular automaton (CA), interpreted as models of unidirectional and bidirectional traffic flow in an ant-trail. The model generalizes the totally asymmetric simple exclusion process (TASEP) taking into account the effect of the pheromone. In this paper, we used the dynamic of TASEP as a model of interaction at a low level of the collective environment in the ant’s traffic flow. This work consists of modifying the movement rules of particles “ants” belonging to the TASEP model, so that it adopts with the natural movement of ants. So, as to respect the constraints of having no more than one particle per a given site, and then avoid collision within a bidirectional circulation, we suggested two strategies: decease strategy and waiting strategy. As a third work stage, this is devoted to the study of these two proposed strategies’ stability. As a final work stage, we applied the first strategy to the whole environment, in order to get to the emergence of traffic flow, which is a way of learning. II. AN OVERVIEW OF ASEP AND TASEP ASEPs, for asymmetric simple exclusion processes are one- dimensional lattice model, with L sites, where particles interact only with hard-core exclusion potential. Each lattice site can be either occupied by a single particle, or empty. During each lapse of time, a particle at the site i attempts to move forward into the up coming site with probability p if the site at the location ) 1 ( + i is empty. At the boundary condition , 1 = i and L i = the dynamic is modified as follow; During each lapse of time, a particle is introduced into the system at site 1 with probability α , if it is site is empty, and leave the system at the site L, with probability β . When the parameter α and β are varied, the model exhibits different phases transition [1][2][3][4] along the line Trace Emergence of Ants’ Traffic Flow, based upon Exclusion Process Ali Lemouari, and Mohamed Benmohamed D World Academy of Science, Engineering and Technology International Journal of Mathematical and Computational Sciences Vol:2, No:7, 2008 507 International Scholarly and Scientific Research & Innovation 2(7) 2008 ISNI:0000000091950263 Open Science Index, Mathematical and Computational Sciences Vol:2, No:7, 2008 publications.waset.org/6047/pdf