This paper tries to analyze the positive effect of the emergent structures in the objects’ aggregation task which is performed by a cognitive multi-agent system (CMAS). Indeed, these structures allow improving overall performance of the system by the optimization of the planning time and satisfaction level of the cognitive agents. A series of simulations enables us to discuss our system.

This paper aims to shed light on the benefits of the cognitive processes in the generation of emergent structures that allow the cognitive robots to succeed the objects' aggregation task. In the multi-robot system, every robot uses local rules and an on-line building and learning of its own cognitive map. This fusion alters the positive impact of the individual behavior in the improvement of the overall system performance. A series of simulations and experiments allowed us to present and discuss the system.

It is assumed that future robots must coexist with human beings and behave as their companions. Consequently, the complexities of their tasks would increase. To cope with these complexities, scientists are inclined to adopt the anatomical functions of the brain for the mapping and the navigation in the field of robotics. While admitting the continuous works in improving the brain models and the cognitive mapping for robots' navigation, we show, in this paper, that learning by imitation leads to a positive effect not only in human behavior but also in the behavior of a multi-robot system. We present the interest of low-level imitation strategy at individual and social levels in the case of robots. Particularly, we show that adding a simple imitation capability to the brain model for building a cognitive map improves the ability of individual cognitive map building and boosts sharing information in an unknown environment. Taking into account the notion of imitative behavior, we also show that the individual discoveries (i.e. goals) could have an effect at the social level and therefore inducing the learning of new behaviors at the individual level. To analyze and validate our hypothesis, a series of experiments has been performed with and without a low-level imitation strategy in the multi-robot system.

In this paper, we present the interest of coupling learning capability and imitation strategy on individual and population levels in the field of Multi-Robot System. Particularly, we show that in an unknown environment adding a simple imitation capability to our bio-inspired architecture leads to a positive effect in the improvement the overall performance of the whole system. Indeed, our motivations is to optimize the robots goals discovery time and to improve the survival rate of agents. To analyze and validate our hypothesis, a series of experiments has been performed with and without a low level imitation strategy in a simulated multi-robot system. We will conclude with robotics' experiments which will feature how our approach applies accurately to real life environments.

This paper tries to analyze and evaluate emergent structures in a multi-agent system which is able to resolve the warehouse location problem. These emergent structures allow agents to optimize their planning time and to improve their adaptive behavior in an unknown environment. In our multi-agent system, each agent is based on an on-line building and learning of its own cognitive map. It alters the positive impact of individual behavior in the improvement of the overall performance of the system. We also suggest the evaluation of the emergent structures by comparing the performance of our multi-agent system with a linear programming approach. A series of simulations enables us to discuss and validate our system.

This paper shows that learning by imitation leads to a positive effect not only in human behavior but also in the behavior of the autonomous agents (AA) in the field of self-organized creation deposits. Indeed, for each agent, the individual discoveries (i.e. goals) have an effect on the performance of the population level and therefore they induce a new learning capability at the individual level. Particularly, we show through a set of experiments that adding a simple imitation capability to our bio-inspired architecture allows increasing the ability of agents to share more information and improving the overall performance of the whole system. We will conclude with robotics’ experiments which will feature how our approach applies accurately to real life environments.

In this paper, we study the impact of the cognitive map's adaptation in the context of multi-robot system. This map governs the emergence of non-trivial behaviors and structures at both individual and social levels. In particular, we show that adding a simple imitation and deposit behavior allows the cognitive robots to adapt themselves in unknown environment to solve different navigation tasks. We show that in our architecture the individual discoveries in each robot (i.e., goals) can have an effect at the population level, which induce then a new learning at the individual level and reciprocally, from the individual to the population level. We performed a series of experimentations with robots and simulated agents to validate our system.

In this paper, we present the interest of low level imitation strategy on individual and population levels in the field of Multi-Robot System. Particularly, we show that adding a simple imitation capability to our bio-inspired architecture boosts the ability of individual cognitive map building. Taking into account the notion of imitative behavior, we also show that the individual discoveries in each robot (i.e. goals) could have an effect on population level and therefore it induces a new learning capability at the individual level. To analyze and validate our hypothesis, a series of experiments have been performed with and without a low level imitation strategy.

This paper describes a clustering process taking inspiration from the cemetery organization of ants. The goal of this paper is (i) to show the importance of the local interactions which allow to produces complex and emergent behavior. (ii) To propose a multi-robot systems in the field of clustering objects allowing optimization of: time of convergence, rate of occupation of the objects in the environment and final number of the clusters. And finally (iii) to propose another system with the use of cognitive robots instead of reactive robots in the same field of clustering objects with generic rules which can be used independently of the environment. Series of simulations enable us to discuss and validate the proposed approach.

Since swarm intelligence allows self-organization into an unfamiliar environment and adapting behaviors through simple individuals' interactions, we propose to realize a swarm multirobot organization with a fuzzy control. We introduce in this paper a fuzzy system for avoiding the collaboration stagnation and to improve the counter-ant algorithm (CAA). The robots' collaborative behavior is based on a hybrid approach combining the CAA and a fuzzy system learned by MAGAD-BFS (Multi-agent Genetic Algorithm for the Design of Beta Fuzzy System). A series of simulations enables us to discuss and validate both the effectiveness of the hybrid approach to the problem of environment exploration (i.e., for the purpose of cleaning an area) as well as the usefulness of MAGAD-BFS for learning the fuzzy knowledge base while tuning it and reducing its number of rules.

This paper presents an evolving method for a self-organizing multirobot exploration of an unknown environment. In such problem, a big consideration is given to the coordination behavior of robots in order to achieve the common tasks in an optimal way. Ant algorithms are proved to be very useful in solving such distributed control problems. We present here a modified version of the known ant algorithm, called Counter-Ant Algorithm (CAA). Indeed, the robots'collective behavior is based on repulsion instead of attraction to pheromone, which is a chemical matter open to evaporation and representing the core of ants' cooperation. A series of experimentations with MINDSTORMS LEGO robots, and simulations under Madkit platform, in laboratory conditions similar to real ones, show the usefulness of our algorithm for self-organizing and cooperative exploration.

The use of multirobot systems, is affecting our society in a fundamental way; from their use in hazardous environments, to their application in automated environmental cleanup. In an unknown environment, one of the most important problem related to multirobot systems, is to decide how to coordinate actions in order to achieve tasks in an optimal way. Ant algorithms are proved to be very useful in solving such distributed control problems. We introduce in this paper a modified version of the known ant algorithm, called Counter-Ant Algorithm (CAA). Indeed, the robots' collaborative behaviour is based on repulsion instead of attraction to pheromone, which is a chemical matter open to evaporation and representing the core of ants' cooperation. In order to test the performance of our CAA, we implement, simulate and test our algorithm in a generic multirobot environment. In practical terms, the subdivision of the cleaning space is achieved in emergent and evolving way. A series of simulations show the usefulness of our algorithm for adaptive and cooperative cleanup.