Graphical abstractDisplay Omitted HighlightsAn algorithm which is able to learn controllers with embedded preprocessing for mobile robotics is presented.Quantified Fuzzy Propositions, a model able to summarize the low-level input data, are used.The algorithm was tested with the wall-following behavior both in simulated and real environments.Results show a better and statistically significant performance of our proposal.The approach was also successfully tested in three real world behaviors. The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.
The approaches based on genetic fuzzy systems use different alternatives in the definition of the membership functions. In @cite_17 @cite_9 @cite_29 the membership functions are defined heuristically. In @cite_23 @cite_12 labels have been uniformly distributed, but the granularity of each input variable is defined using expert knowledge. On the other hand, in @cite_5 @cite_31 @cite_22 @cite_24 @cite_0 an approximative approach is used, i.e., different membership functions are learned for each rule, reducing the interpretability of the learned controller.
@cite_22 @cite_9 @cite_29 @cite_24 @cite_0 @cite_23 @cite_5 @cite_31 @cite_12 @cite_17
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We propose a learning automata-based method for optimizing membership functions.The proposed method adjusts the number and the position of membership functions.The proposed method can be used without any change in any fuzzy recommender system.The performance of proposed method is tested on well-known datasets.The results show that the proposed method improves the recommendation accuracy. Incorporating trust and distrust information into collaborative recommender systems alleviates data sparsity and cold start problems. Since trust and distrust are a gradual phenomenon, they can be stated more naturally by fuzzy logic. Finding the most appropriate fuzzy sets which cover the domains of trust and distrust is not an easy task. Existing research on fuzzy modelling of trust and distrust has not considered the optimization of membership functions. In this paper, we address this issue and propose a continuous action-set learning automata (CALA)-based method to adjust membership functions of fuzzy trust and distrust during the lifetime of recommender system in terms of recommendation error. By assigning a CALA to the centre parameter of each triangular membership function, the proposed method optimizes the number and the position of fuzzy sets. To the best of our knowledge, this is the first effort in this direction. The experimental results indicate that using the proposed method in fuzzy recommender systems improves the recommendation accuracy. In complex systems it often occurs that relevant infor- mation about the system state and behavior is provided by groups of low-level variables rather than single variables. This grouping into high-level variables introduces a hierachy in the knowledge that can only be captured by means of rules involving propositions with a rep- resentation capability that is more complex than usual ones. In this paper we describe a genetic programming based approach for auto- mated learning of Quantified Fuzzy Rules that are capable to deal with such representation capability. An application of this approach for hierarchical grouping of the distance measures provided by the laser sensors of a mobile robot (for the wall-following behaviour) is presented. Experimentation results show the control action is ac- ceptable although no prior knowledge on the variables definition and structure was introduced in the controller. We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time, determined by exact numerical membership degrees, even in the presence of uncertainty in the data being analyzed. We create an algorithm for determining the optimal membership degrees with respect to a given goal function. Based on the membership degrees, we introduce a measure that is able to identify outlier vertices that do not belong to any of the communities, bridge vertices that have significant membership in more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The method can also be used for prediction in case of uncertainty in the data set analyzed. The number of communities can be given in advance, or determined by the algorithm itself, using a fuzzified variant of the modularity function. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to, social networks, scientific collaboration networks, and cortical networks, with high confidence. Conventional fuzzy logic controller is applicable when there are only two fuzzy inputs with usually one output. Complexity increases when there are more than one inputs and outputs making the system unrealizable. The ordinal structure model of fuzzy reasoning has an advantage of managing high-dimensional problem with multiple input and output variables ensuring the interpretability of the rule set. This is achieved by giving an associated weight to each rule in the defuzzification process. In this work, a methodology to design an ordinal fuzzy logic controller with application for obstacle avoidance of Khepera mobile robot is presented. The implementation will show that ordinal structure fuzzy is easier to design with highly interpretable rules compared to conventional fuzzy controller. In order to achieve high accuracy, a specially tailored Genetic Algorithm (GA) approach for reinforcement learning has been proposed to optimize the ordinal structure fuzzy controller. Simulation results demonstrated improved obstacle avoidance performance in comparison with conventional fuzzy controllers. Comparison of direct and incremental GA for optimization of the controller is also presented.