A Neuro-fuzzy Approach for Hierarchical Behaviour Learning
نویسندگان
چکیده
In autonomous navigation of mobile robots, the dynamic environment is a source of problems. Because it is not possible to model all the possible conditions, the key point in the robot control is to design a system that is adaptable to different conditions. This paper first describes a behaviour hierarchy, which decomposes behaviours of a robot, needed to achieve a goal, into simpler behaviours. Then explains a neuro-fuzzy approach to learn fuzzy rules and to model fuzzy systems. Finally a combination of these two approaches for learning hierarchical fuzzy behaviours is discussed.
منابع مشابه
Emotional Learning to Control Large-scale Systems
In this paper, with a new look at emotional controller and modifying its structure; a novel approach to hierarchical control of large-scale systems is introduced. Design of controller is founded on emotional learning and the control system consists of neuro-fuzzy controller, whose weights are updated according to emotional signals. This signal is produced in a block called critic, whose job is ...
متن کاملHierarchical Neuro-Fuzzy Systems Part II
This paper describes a new class of neuro-fuzzy models, called Reinforcement Learning Hierarchical NeuroFuzzy Systems (RL-HNF). These models employ the BSP (Binary Space Partitioning) and Politree partitioning of the input space [Chrysanthou,1992] and have been developed in order to bypass traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity t...
متن کاملHierarchical Neuro-Fuzzy Systems Part I
Neuro-fuzzy [Jang,1997][Abraham,2005] are hybrid systems that combine the learning capacity of neural nets [Haykin,1999] with the linguistic interpretation of fuzzy inference systems [Ross,2004]. These systems have been evaluated quite intensively in machine learning tasks. This is mainly due to a number of factors: the applicability of learning algorithms developed for neural nets; the possibi...
متن کاملHierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP- MD) and a MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph
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 neuro-fuzzy multiagent systems that use MultiAgent Reinforcement Learning...
متن کاملReinforcement Learning Hierarchical Neuro-Fuzzy Politree Model for Control of Autonomous Agents
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), and it...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003