H-FQL: A new reinforcement learning method for automatic hierarchization of fuzzy systems: An application to the route choice problem

Dai, Kais; Kammoun, Habib M.; Alimi, Adel M.

doi:10.1109/is.2012.6335114

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…The combination of FLSs and Q‐learning algorithm was used successfully in the works of Muñoz et al() for dynamic traffic steering and load balancing in wireless networks and in the work of Chen et al for admission control in heterogeneous wireless networks. A hierarchical extension was described in the work of Dai et al…”

Section: Basic Conceptsmentioning

confidence: 99%

A hybrid trust model using reinforcement learning and fuzzy logic

Aref

Tran

2017

Computational Intelligence

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Multiagent systems (MASs) are increasingly popular for modeling distributed environments that are highly complex and dynamic, such as e‐commerce, smart buildings, and smart grids. Typically, agents assumed to be goal driven with limited abilities, which restrains them to working with other agents for accomplishing complex tasks. Trust is considered significant in MASs to make interactions effectively, especially when agents cannot assure that potential partners share the same core beliefs about the system or make accurate statements regarding their competencies and abilities. Due to the imprecise and dynamic nature of trust in MASs, we propose a hybrid trust model that uses fuzzy logic and Q‐learning for trust modeling. as an improvement over Q‐learning‐based trust evaluation. Q‐learning is used to estimate trust on the long term, fuzzy inferences are used to aggregate different trust factors, and suspension is used as a short‐term response to dynamic changes. The performance of the proposed model is evaluated using simulation. Simulation results indicate that the proposed model can help agents select trustworthy partners to interact with. It has a better performance compared to some of the popular trust models in the presence of misbehaving interaction partners.

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Section: Basic Conceptsmentioning

confidence: 99%

A hybrid trust model using reinforcement learning and fuzzy logic

Aref

Tran

2017

Computational Intelligence

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“…The idea of using machine learning to build a robot controller while interacting with the environment is certainty not a new one. The combination of learning algorithms and robotics is seen as a very attractive mix and just about every machine learning technique has been applied including Artificial Neural Networks (ANNs) (Shi et al 2013;Mnih et al 2013), Linear Weighted Regression (Schaal et al 2000;Smart and Kaelbling 2002) and Fuzzy Systems (Hagras et al 2001;Guanloa et al 2004;Dai et al 2012). The majority of the research conducted into finding an effective representation have focused on two approaches.…”

Section: Adaptive Representationmentioning

confidence: 99%

Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

Waldock

Carse

2015

Soft Comput

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The majority of machine learning techniques applied to learning a robot controller generalise over either a uniform or pre-defined representation that is selected by a human designer. The approach taken in this paper is to reduce the reliance on the human designer by adapting the representation to improve the generalisation during the learning process. An extension of a Hierarchical Fuzzy Rule-Based System (HFRBS) is proposed that identifies and refines inaccurate regions of a fuzzy controller, while interacting with the environment, for both supervised and reinforcement learning problems. The paper shows that a controller using an adaptive HFRBS can learn a suitable control policy using a fewer number of fuzzy rules for both a supervised and reinforcement learning problem and is not sensitive to the layout as with a uniform representation. In supervised learning problems, a small number of extra trials are required to find an effective representation but for reinforcement learning problems, the process of adapting the representation is shown to significantly reduce the time taken to learn a suitable control policy and hence open the door to high-dimensional problems.

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H-FQL: A new reinforcement learning method for automatic hierarchization of fuzzy systems: An application to the route choice problem

Cited by 2 publications

References 23 publications

A hybrid trust model using reinforcement learning and fuzzy logic

A hybrid trust model using reinforcement learning and fuzzy logic

Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

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