title: Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.
abstract: This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.
authors.affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro - RJ, Brazil.
authors.forename: Leonardo Forero
authors.initials: LF
authors.lastname: Mendoza
authors.forename: Marley
authors.initials: M
authors.lastname: Vellasco
authors.forename: Karla
authors.initials: K
authors.lastname: Figueiredo
mesh_terms: D001185:Artificial Intelligence; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology
keywords: Multiagent coordination; intelligent agents; neuro-fuzzy; reinforcement learning