Paper Info
Title
Reinforcement Learning Hierarchical Neuro-Fuzzy Politree Model for Control of Autonomous Agents
Abstract
This work presents a new hybrid neuro-fuzzy model for automatic learning of actions taken by agents. The main objective of this new model is to provide an agent with intelligence, making it capable, by interacting with its environment, to acquire and retain knowledge for reasoning (infer an action). This new model, named Reinforcement Learning Hierarchical Neuro-Fuzzy Politree (RL-HNFP), descends from the Reinforcement Learing Hierarchical Neuro-Fuzzy BSP (RL-HNFB) that uses Binary Space Partitioning. By using hierarchical partitioning methods, together with the Reinforcement Learning (RL) methodology, a new class of Neuro-Fuzzy Systems (SNF) was obtained, which executes, in addition to automatically learning its strucutre, the autonomous learning of the actions to be taken by an agent. These characteristics represent an important differential when compared with the existing intelligent agents learning systems. The obtained results demonstrate the potential of this new model, which operates without any prior information, such as number of rules, rules specification, or number of partitions that the input space should have.
Year
DOI
Venue
2004
10.1109/ICHIS.2004.80
HIS
Keywords
DocType
ISBN
Autonomous Agents,new model,new class,learning,Reinforcement Learning Hierarchical Neuro-Fuzzy,neuro-fuzzy systems,autonomous learning,existing intelligent agent,automatic learning,Neuro-Fuzzy Systems,Reinforcement Learing Hierarchical Neuro-Fuzzy,Reinforcement Learning,reinforcement learning,Politree Model,new hybrid neuro-fuzzy model
Conference
0-7695-2291-2
Citations 
PageRank 
References 
6
0.60
8
Authors
4
Name
Order
Citations
PageRank
Karla Figueiredo1306.53
Marley B. R. Vellasco228047.47
Marco Pacheco3101.56
Flávio Joaquim De Souza4775.56