The hippocampus as a cognitive map

Research Advance in Swarm Robotics

The main goal of this paper is to provide an overview of the rapidly
 developing area of software agents serving as a reference point to a large
 body of literature and to present the key concepts of software agent
 technology, especially agent languages, tools and platforms. Special
 attention is paid on significant languages designed and developed in order to
 support implementation of agent-based systems and their applications in
 different domains. Afterwards, in the paper a number of useful and
 practically used tools and platforms that are available and support
 activities or phases of the process of agent-oriented software development
 are presented.

/pdf/software-agents-languages-tools-platforms-4l7ae696x4.pdf

Software agents: Languages, tools, platforms

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.

/pdf/adapt-traf-an-adaptive-multiagent-road-traffic-management-109iuhweq5.pdf

Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development

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.

/pdf/counter-ant-algorithm-for-evolving-multirobot-collaboration-4jgt4bvs3a.pdf

Counter-ant algorithm for evolving multirobot collaboration

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.

/pdf/adaptation-capability-of-cognitive-map-improves-behaviors-of-3z5pqss4px.pdf

Adaptation capability of cognitive map improves behaviors of social robots

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.

/pdf/self-organizing-multirobot-exploration-through-counter-ant-168f1g7qnm.pdf

Self-Organizing Multirobot Exploration through Counter-Ant Algorithm

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.

/pdf/emergent-complex-behaviors-for-swarm-robotic-systems-by-53hptdr0qg.pdf

Emergent complex behaviors for swarm robotic systems by local rules

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...

The effect of learning by imitation on a multi-robot system based on the coupling of low-level imitation strategy and online learning for cognitive map building

Abdelhak Chatty

Papers

Counter-ant algorithm for evolving multirobot collaboration

Adaptation capability of cognitive map improves behaviors of social robots

Self-Organizing Multirobot Exploration through Counter-Ant Algorithm

Emergent complex behaviors for swarm robotic systems by local rules

The effect of learning by imitation on a multi-robot system based on the coupling of low-level imitation strategy and online learning for cognitive map building