Name
Title | ||
|---|---|---|
Multiagent Coordination Systems Based On Neuro-Fuzzy Models With Reinforcement Learning |
Abstract | ||
|---|---|---|
This paper presents the research and development of a hybrid neuro-fuzzy model 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-GC). 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLA.2018.00151 | 2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) |
Keywords | Field | DocType |
Multiagent coordination, reinforcement learning, neuro-fuzzy, intelligent agents. Introduction (Heading 1) | Complex system,Intelligent agent,Neuro-fuzzy,Task analysis,Computer science,Multi-agent system,Redundancy (engineering),Artificial intelligence,Robot,Machine learning,Reinforcement learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonardo Alfredo Mendoza | 1 | 0 | 0.34 |
| Evelyn Batista | 2 | 0 | 0.34 |
| Harold Dias De Mello | 3 | 0 | 0.34 |
| Marco Aurélio Cavalcanti Pacheco | 4 | 143 | 22.29 |