Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICSMC.2010.5642425 | Systems Man and Cybernetics |
Keywords | Field | DocType |
fuzzy control,fuzzy systems,genetic algorithms,groupware,learning (artificial intelligence),multi-agent systems,multi-robot systems,adapting behavior,collaboration stagnation,collaborative behavior,fuzzy control,fuzzy counter ant,fuzzy knowledge base,fuzzy system,multirobot exploration,swarm intelligence,swarm multirobot organization,MAGAD-BFS,collaboration,counter-ant algorithm,fuzzy system,multirobot,pheromone,stagnation recovery | Neuro-fuzzy,Swarm behaviour,Computer science,Swarm intelligence,Fuzzy logic,Multi-agent system,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Genetic algorithm,Machine learning | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-4244-6586-6 | 1 |
PageRank | References | Authors |
0.36 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chatty, A. | 1 | 4 | 1.22 |
| Ilhem Kallel | 2 | 12 | 1.65 |
| Adel M. Alimi | 3 | 81 | 8.88 |
| Philippe Gaussier | 4 | 29 | 4.44 |